Identification of Hydrothermal Alteration Minerals for Exploring

Article Identiﬁcation of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong

Kai Xu 1,2,3, Xiaofeng Wang 1, Chunfang Kong 1,2,4,* , Ruyi Feng 1, Gang Liu 1 and Chonglong Wu 1,2 1 School of Computer, China University of Geosciences, Wuhan 430074, China; [email protected] (K.X.); [email protected] (X.W.); [email protected] (R.F.); [email protected] (G.L.); [email protected] (C.W.) 2 Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang 550081, China 3 Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education, Wuhan 430074, China 4 National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang 421000, China * Correspondence: [email protected]; Tel.: +86-027-6788-3286

Received: 3 December 2019; Accepted: 11 December 2019; Published: 13 December 2019

Abstract: Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

Keywords: gold deposit; alteration information; ASTER image; support vector machine (SVM); principal component analysis (PCA)

1. Introduction Surrounding rock alteration is one of the important interpretation markers of mineral exploration using remote sensing technology [1,2]. The metasomatism of hydrothermal mineralization can cause the minerals to alter and generate groups or ions such as Fe3+, magnesium hydroxyl group and aluminum hydroxyl group, which show different hue and spectral characteristics compared to the non-altered rock in remote sensing images [3–5]. In the process of mineral exploration, according to the different hue and reflectance spectra of altered minerals, the composition and spatial distribution of altered minerals can be analyzed using remote sensing technology, and the favorable metallogenic areas can be found out. With the development of remote sensing technology, most scholars have focused on the mechanism of the mineral spectral [6–9]. Others have studied extraction method of altered mineral [10–12]. Still, some have researched the identification of hydrothermal alteration minerals using multi- and hyper-spectral images [13–16]. In recent years, altered mineral identification has been investigated by

Remote Sens. 2019, 11, 3003; doi:10.3390/rs11243003 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 3003 2 of 22 using different methods in different locations for mineral exploration, including alteration mineral Remote Sens. 2019, 11, x FOR PEER REVIEW 2 of 23 mapping in the Northwestern Junggar basin using Landsat thematic mapper (TM) data and principal componentalteration analysis mineral (PCA) mapping [17], in predictive the Northwestern mineral Junggar prospectivity basin using modeling Landsat for thematic Cu deposits mapper in Varzaghan (TM) district,data NW and Iranprincipal based com onponent the support analysis vector (PCA) [ machine17], predictive (SVM) mineral [18], asprospectivity well as predictive modeling modelsfor Cu for Rodalquilardeposits mining in Varzaghan district district, mineral NW prospectivity Iran based on in the the support southeast vector of Spainmachine with (SVM) machine [18], as learning well as [19]. All ofpredictive these studies models contribute for Rodalquilar to the mining development district mineral of metallogenic prospectivity prognosis in the southeast in arid of Spain and semi-arid with areas.machine However, learning research [19]. on All altered of these mineral studies extractioncontribute to in the high development vegetation-covered of metallogenic areas prognosis is still limited, particularlyin arid in and a specific semi-arid region areas. as However, an important research metal on altered mineral mineral area [20 extraction,21], where in high many vegetation old mines- are covered areas is still limited, particularly in a specific region as an important metal mineral area facing resource depletion after long-term exploitation, such as Gulong, China. [20,21], where many old mines are facing resource depletion after long-term exploitation, such as In response to the above problems, the objectives of this paper are to: (1) select the image Gulong, China. identificationIn response marks of to altered the above minerals problems, according the objective to the distributions of this paper of gold aredeposits to: (1) select and the the ore-bearing image strataidentification in Dayaoshan; marks (2) of remove altered vegetation minerals according interference to the by distribution using mixed of gold pixel deposits decomposition and the ore method- withbearing hyperplane strata andin Dayaoshan; genetic algorithm (2) remove (GA) vegetation optimization; interference (3) by extract using mixed altered pixel mineral decomposition information basedmethod on PCA with and hyperplane SVM methods and gene by thetic algorithm Advanced (GA) Spaceborne optimization; Thermal (3) extract Emission altered and mineral Reflection Radiometerinformation (ASTER) based data; on PCA and and (4)verify SVM method the altereds by mineralthe Advanced extraction Spaceborne result usingThermal field Emission survey and method. Lastly,Reflection the research Radiometer results (ASTER) not only data; delineate and (4) the verify favorable the altered metallogenic mineral extraction area of gold result deposits, using field but also providesurve reasonabley method. suggestionsLastly, the research for mineral results explorationnot only delineate with the remote favorable sensing metallogenic data. area of gold deposits, but also provide reasonable suggestions for mineral exploration with remote sensing data. 2. Study Area and Materials 2. Study Area and Materials The study area, located at the junction of the Yangtze paleo-plate and Cathaysia paleo-plate, is The study area, located at the junction of the Yangtze paleo-plate and Cathaysia paleo-plate, is part ofpart the of Guangxi the Guangxi Dayaoshan Dayaoshan Au Au ore ore belt, belt, dotted dotted with with gold gold depositsdeposits [22–2525]] (Fig (Figureure 11).). It It belongs belongs to the subtropicalto the subtropical monsoon monsoon climate climate region, region, where where the the annual annual average average temperature temperature isis 2121°C,◦C, the the frost frost-free- daysfree are aboutdays are 320 about days, 320 and days, the and average the average annual annual rainfall rainfall is 1600 is 1600 mm. mm. There There are are a lot a oflot pristineof pristine forests and allforests kinds and of all herbaceous kinds of herbaceous plants, and plants, forest and cover forest can cover reach can morereach thanmore 90%.than 90%.

(a)

Figure 1. Cont.

Remote Sens. 2019, 11, 3003 3 of 22

Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 23

(b)

(c)

(d)

Figure 1. Advanced Spaceborne Thermal Emission and Reﬂection Radiometer (ASTER) image of the study area (a), regional geological sketch map (b) (from the Remote Sensing Center of Guangxi), located in Guangxi (c) and in China (d).

The study area is strongly inﬂuenced by multistage structural evolution, such as sedimentation, uplift, and folds and faults in diﬀerent directions in a long process of geological evolution, which makes Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 23

Figure 1. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image of the study area (a), regional geological sketch map (b) (from the Remote Sensing Center of Guangxi), located in Guangxi (c) and in China (d). Remote Sens. 2019, 11, 3003 4 of 22 The study area is strongly influenced by multistage structural evolution, such as sedimentation, uplift, and folds and faults in different directions in a long process of geological evolution, which the structures of the research area very complex. It is located in the anticline axis of the Gulong-Ligang makes the structures of the research area very complex. It is located in the anticline axis of the Gulong- Indochina period. The north-south anticline strikes and superimposes on the Caledonian near-east-west Ligang Indochina period. The north-south anticline strikes and superimposes on the Caledonian fold, which has a length of 28 km and width of about 2–5 km. There are two circular structures in near-east-west fold, which has a length of 28 km and width of about 2–5 km. There are two circular the study area, which are distributed in the Dacun and Gulong. The single ring is approximately structures in the study area, which are distributed in the Dacun and Gulong. The single ring is circular and about 2-5km in diameter. There are fault structures in the study area, which are near approximately circular and about 2-5km in diameter. There are fault structures in the study area, the south-north direction of the Limu-Majiang fault, with a total length of more than 200 km. The which are near the south-north direction of the Limu-Majiang fault, with a total length of more than southern section of the Limu-Majiang fault plays an important role in controlling gold deposits. The 200 km. The southern section of the Limu-Majiang fault plays an important role in controlling gold northeast direction of Pingxiang-Dali fault, which passes through study area, plays an important role in deposits. The northeast direction of Pingxiang-Dali fault, which passes through study area, plays an controlling altered rock type and volcanic type gold deposit. The strata exposed are mainly Cambrian, important role in controlling altered rock type and volcanic type gold deposit. The strata exposed are Cretaceous, Devonian and a spot of Quaternary. The main intrusive rocks are mainly Caledonian mainly Cambrian, Cretaceous, Devonian and a spot of Quaternary. The main intrusive rocks are granitic porphyry veins and granodiorite veins; Yanshanian granite-porphyry, granodiorite, quartz mainly Caledonian granitic porphyry veins and granodiorite veins; Yanshanian granite-porphyry, diorite, and monzonitic granite; and Indosinian gabbro diorite, which plays a vital role in formation of granodiorite, quartz diorite, and monzonitic granite; and Indosinian gabbro diorite, which plays a porphyry deposits of the whole study area (Figure1b). vital role in formation of porphyry deposits of the whole study area (Figure 1b). At present, the endophytic metal deposits found in the study area can be divided into porphyry At present, the endophytic metal deposits found in the study area can be divided into porphyry deposits, broken zone altered rock deposits, and quartz vein deposits. Those three kinds of deposits deposits, broken zone altered rock deposits, and quartz vein deposits. Those three kinds of deposits are often associated with each other, that is to say, there are altered rock deposits and quartz vein are often associated with each other, that is to say, there are altered rock deposits and quartz vein deposits of diﬀerent scales in the outer part of the porphyry deposits (Figure1b). deposits of different scales in the outer part of the porphyry deposits (Figure 1b). The ASTER images adopted in this paper are from the Remote Sensing Center of Guangxi, acquired The ASTER images adopted in this paper are from the Remote Sensing Center of Guangxi, in September 2001, and the RGB band combination of ASTER image is as follows: R is B2, G is B1, acquired in September 2001, and the RGB band combination of ASTER image is as follows: R is B2, and B is (3*b1 + b3)/4 (Figure1a). Before identifying the altered mineral, the images were pretreated, G is B1, and B is (3*b1 + b3)/4 (Figure 1a). Before identifying the altered mineral, the images were such as atmospheric correction by the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes pretreated, such as atmospheric correction by the Fast Line-of-sight Atmospheric Analysis of Spectral (FLAASH) [26,27], crosstalk correction [28], water disturbance removed by using a water index. The Hypercubes (FLAASH) [26,27], crosstalk correction [28], water disturbance removed by using a water ﬂow chart of ASTER image preprocessing is shown in Figure2. index. The flow chart of ASTER image preprocessing is shown in Figure 2.

ASTER images

Atmospheric correction Crosstalk removed Water disturbance removed

Result of preprocessing

Figure 2. Flow chart of ASTER image preprocessing. Figure 2. Flow chart of ASTER image preprocessing. 3. Methods 3. Methods The extraction of altered mineral information is usually based on the spectral characteristics of mineralsThe [extraction9,29–31]. Although of altered minerals mineral haveinformation their own is spectralusually characteristics,based on the spectral rocks are characteristics usually formed of byminerals various [9 mineral,29–31]. assemblages, Although minerals so the spectral have their information own spectral of rocks characteristics, on remote sensing rocks images are usually is very complex.formed by In various addition, mineral due to assemblages, the influence ofso other the spec surfacetral information features, such ofas rocks soil and on remote vegetation, sensing the alteredimages mineralis very complex. information In addition, is very weak due intothe the remote influence sensing of other images surface [32– features,34]. Therefore, such as this soil paper and firstlyvegetation, focuses the on altered eliminating mineral vegetation information interference, is very extracting weak in mineralized the remote information sensing images samples [32 with–34]. remoteTherefore, sensing this paper images, firstly and focuses then establishing on eliminating PCA andvegetation SVM methods interference, to improve extracting the mineralized accuracy of alteredinformation mineral samples information with remote extraction. sensing images, and then establishing PCA and SVM methods to improve the accuracy of altered mineral information extraction. 3.1. Mixed Pixel Decomposition with Hyperplane Optimized by GA 3.1. Mixed Pixel Decomposition with Hyperplane Optimized by GA In areas with high vegetation coverage, the spectral characteristics of pixels in remote sensing imagesIn area are nots with a single high vegetation ground object, coverage, but athe mixed spectral reflection characteristics of the several of pixels objects. in remote The sensing altered mineralimages are information not a single is ground seriously object, hided but by a highmixed vegetation reflection coverage,of the several soit objects. is very The diffi alteredcult to mineral extract theinformation information is seriously which is needed. hided by For high mixed vegetation pixels, an coverage, unmixed so pixel it is decomposition very difficult to method extract with the hyperplaneinformation and which an isoptimized needed. genetic For mixed algorithm pixels, [ an35 – unmixed39], which pixel is established decomposition in this method paper, with will eliminate the influence of vegetation, and obtain altered mineral information from the sub-pixel level. In the feature space of a remote sensing image, the same kind of mineral is assembled in one region because of its similar spectral characteristics, while other similar minerals are gathered in another Remote Sens. 2019, 11, 3003 5 of 22 region. By calculating the hyperplane, these kinds of features can be separated, and the proportion of the objects contained in each pixel can be obtained, that is, the decomposition result of the mixed pixels, which can be expressed as Equation (1):

f (x) = M X > 0, f (x) = M X 0 (1) ∗ ∗ ≤ where M = m1, m2, ..., mn, X = (x1, x2, ... , xn), n is the dimension of feature space. The position of hyperplane in multidimensional remote sensing data space can be well represented by iterative recursive mode, and the equation is expressed as Equation (2):

d = αN cos αN 1 + βN 1 sin αN 1 − − − βN 1 = αN 1 cos αN 2 + βN 2 sin αN 2 − − − − − βN 2 = αN 2 cos αN 3 + βN 3 sin αN 3 (2) − − . − − − . β1 = α1 cos α0 + β0 sin α0 where αN 1 is the angle between the hyperplane unit normal and the XN, αN 2 is the angle between − − the normal projection in the X1, X2, ... , XN 1 space and the XN 1 axis, αN 3 is the angle between the − − − normal projection in the X1, X2, ... , XN 2 space and the XN 2 axis, α1 and α0 is the angle between − − the normal projection of 2D space and the 2nd feature axis, and 1D space and the 1st feature axis, respectively. If α0 = 0, d is the vertical distance between the hyperplane and the origin, in this way, a hyperplane of N-dimensional space can be determined by N 1 angle α1, α2, ... , αN 1 and a vertical − − distance d [40]. It can be seen in Equation (2) that if hyperplane is used to classify the feature space composed of multi-band remote sensing images, the first problem that needs to be solved is how to determine the N parameters of the hyperplane equation, which can best achieve the classification effect. In this paper, to establish a hyperplane classification model optimized by the genetic algorithm for classification, firstly, the pattern description and pattern matching of the samples in the training sample set are carried out; secondly, comparison and selection of different pattern schemes with evolution are performed, and the best pattern classification scheme is chosen; and finally, this is extended to the whole image to achieve the purposeRemote Sens. of pattern2019, 11, x classification.FOR PEER REVIEW This process is shown in Figure3. 6 of 23

Mixed pixel decomposition with genetic optimization for hyperplanar

Hyperplane Genetic algorithm classification model

Parameter initialization Test data Random Genetic programming selection of some Group initialization selection of hyperplane sets hyperplanar sets Genetic operation Optimal Introducing classifier Calculate classification Fitness evaluation One of accuracy Remote the best sensing Optimal individual hyperpla image nar sets Meet the final Evolutionary Remote sensing image conditions of termination classification Y evolution

N Classification results Genetic operation

FigureFigure 3. Mixed 3. Mixed pixel pixel decomposition decomposition with with genetic-hyperplanegenetic-hyperplane flow ﬂow chart chart..

3.2. Mineral Altered Samples Collection with Ratio Analysis and PCA By using the ratio of reflection and absorption band, the spectral difference among various geological information can be enhanced, and the influence of topography can be reduced [41–44]. Therefore, according to the characteristics of the spectrum, the selection of appropriate ASTER band for ratio operation can enhance the weak altered mineral. Because of high correlation among the bands of images, some of the data are redundant and repetitive [13,45–47]. In this paper, PCA of images is described, and the independent quantities obtained in order to reduce the correlation influence in the process of alteration information extraction are recorded. PCA combines the original n features linearly and establishes m principal components, which are not correlated with each other, and the variance decreases gradually. The expression of the transformation is as follow Equation (3): 푛

Y = AX = ∑ 퐴푖푋푖 (3) 푖=1 Transformation is to reduce the dimension of the original data, using m-dimensional vector can cover the original n-dimensional information, and make its standard deviation within the allowable range, then there is Equation (4): m n

Y = AX = ∑ AiXi + ∑ AiXi (4) i=1 i=m+1

Where Xi is denoted as bi, then the Equation (4) becomes Equation (5): m n

Y(m) = ∑ AiX + ∑ Aibi (5) i=1 i=m+1 Let the error is ε between Y and Y(m), n

ε = ∑ A(Xi − bi) (6) i=m+1

Remote Sens. 2019, 11, 3003 6 of 22

3.2. Mineral Altered Samples Collection with Ratio Analysis and PCA By using the ratio of reflection and absorption band, the spectral difference among various geological information can be enhanced, and the influence of topography can be reduced [41–44]. Therefore, according to the characteristics of the spectrum, the selection of appropriate ASTER band for ratio operation can enhance the weak altered mineral. Because of high correlation among the bands of images, some of the data are redundant and repetitive [13,45–47]. In this paper, PCA of images is described, and the independent quantities obtained in order to reduce the correlation influence in the process of alteration information extraction are recorded. PCA combines the original n features linearly and establishes m principal components, which are not correlated with each other, and the variance decreases gradually. The expression of the transformation is as follow Equation (3):

Xn Y = AX = AiXi (3) i=1

Transformation is to reduce the dimension of the original data, using m-dimensional vector can cover the original n-dimensional information, and make its standard deviation within the allowable range, then there is Equation (4):

Xm Xn Y = AX = AiXi + AiXi (4) i=1 i=m+1 where Xi is denoted as bi, then the Equation (4) becomes Equation (5):

Xm Xn Y(m) = AiX + Aibi (5) i=1 i=m+1

Let the error is ε between Y and Y(m),

Xn ε = A(X b ) (6) i − i i=m+1

3.3. Altered Mineral Extraction Using SVM and ACA To improve the extraction accuracy of mineral alteration information, the SVM model, an excellent remote sensing image classification technology [48–52], was trained using alteration sample data by PCA. SVM firstly maps samples to high-dimensional spaces by nonlinear transformation; and then finds out the optimal classification hyperplane in high-dimensional space; and finally, classifies the sample data. The samples are set (X ,Y ), i = 1, 2, 3, ... , n, Y 1, 1 , ωX + b = 0 is the classified hyperplane i i i ∈ {− } of the sample, so construct discriminant function (7):

g(x) = ωX + b (7) where ω represents the normal vector, b represents the displacement, which determines the distance between the hyperplane and the origin. For the optimal classiﬁcation hyperplane, the SVM is:

1 2 min 2 ω ω,b || || (8) s.t.y ωT x + b 1, i = 1, 2, ... , l i· · i ≥ Remote Sens. 2019, 11, 3003 7 of 22

For nonlinear data samples, relaxation variables (ξi) and penalty factors (C) are introduced to deal with the error problem, Equation (8) becomes Equation (9).

l 1 2 P min 2 ω + C ξi ω,b || || (9) i=1 s.t.y ωT x + b 1 ξ , i = 1, 2, ... , l i· · i ≥ − i In order to map the sample to the higher dimensional feature space, and to avoid the diﬃculty of calculation, the Gaussian kernel function is introduced.

2 k x , x = exp γ x , x (10) i j − i j where = 1 γ 2σ2 Classiﬁcation decision function of optimal classiﬁcation hyperplane is Equation (11).   Xl    h(x) = sgn ai yik xi, xj + b (11)   j=1

In summary, determining the parameters C and γ of SVM is the key to improve the classiﬁcation accuracy. So the ACA is introduced to solve the problem of SVM parameter optimization [53–57]. ACA, as a heuristic algorithm based on global optimization, can solve the parameter optimization problem very well. The optimization operation steps are as follows: (1) parameter search interval, C [0, 10], γ [0, 1] is determined; (2) the process is initialized, each ant carries on the random ∈ ∈ allocation operation, named C, γ , the grid point on each space corresponds to one state, and each state corresponds to a solution on the space, the movement trajectory of ants in the grid is recorded, and the pheromone is arranged according to the objective function value between each grid point, so that the ant can search according to the size of the pheromone on the grid point; and (3) the root mean square error is set as the objective function, and then the best combination is selected among the many parameterRemote Sens. combinations. 2019, 11, x FOR PEER The REVIEW optimization algorithm ﬂow of ACA is shown in Figure4. 8 of 23

AOC and SVM parameters initialization

Ant search

SVM sample set setting

Objective function calculation

Maximum number N of iterations

Y SVM optimal parameters.

FigureFigure 4. 4.Flow Flow chart chart of of the the support support vector vector machine machine (SVM) (SVM) parameters parameters with with the the ant ant colony colony algorithm algorithm (ACA). (ACA).

ToTo sum sum up, up, the the steps steps of remote of remote sensing sensing altered altered mineral mineral information information extraction extraction with PCA with and PCA SVM and methodsSVM methods are as follows: are as follows: (1) The remote1) The sensing remote images sensing in theimages study in area the arestudy preprocessed, area are preprocessed, including radiometric including calibration,radiometric atmosphericcalibration, correction atmospheric and descrambling, correction and and eliminate descrambling, the inﬂuence and of eliminate vegetation the with influence mixed of pixel decompositionvegetation with method. mixed pixel decomposition method. 2) The threshold density segmentation of pyrite, sericite and chlorite principal components is carried out with ratio analysis and PCA, and the training samples are collected according to the known deposits. 3) The kernel parameters and penalty factors of SVM with ACA are optimized. 4) The SVM classifier trained by training samples is used to process the remote sensing image to obtain the abnormal information of altered minerals.

4. Results and Discussion

4.1. Vegetation Disturbance Elimination In the study area, there are three main land cover types, which are vegetation, rock and water. The selection of training samples was based on the field survey, remote sensing image, and topographic map. According to the results of the field survey, 52 field rock sampling regions in the image were used as sampling region of rock, and 50–200 pixels were selected as rock samples in each region, which have a total of 6320 rock samples. Then in the vegetation coverage region surrounding the 52 rock sampling regions, and 50–500 pixels were selected as vegetation samples in each region, which have a total of 8460 vegetation samples. Finally, 50–100 pixels of water samples were selected for each of the 50 water regions on the image, which have a total of 4210 water samples. The training samples and reference samples were carried out in ENVI4.8, with 80% of them used as training samples and the remaining 20% as test samples. The details are shown in Table 1.

Table 1. Training samples.

Band Type Number of Samples Total Number C1 vegetation 8460 C2 rock 6320 18,990 C3 water 4210 Three bands of remote sensing image were selected, and three kinds of training samples were classified by using two-dimensional feature space. According to Equation (2), the hyperplane equation can be simplified to:

d = 푋2 × cos 훼 + 푋1 × sin 훼 (12)

Remote Sens. 2019, 11, 3003 8 of 22

(2) The threshold density segmentation of pyrite, sericite and chlorite principal components is carried out with ratio analysis and PCA, and the training samples are collected according to the known deposits. (3) The kernel parameters and penalty factors of SVM with ACA are optimized. (4) The SVM classiﬁer trained by training samples is used to process the remote sensing image to obtain the abnormal information of altered minerals.

4. Results and Discussion

4.1. Vegetation Disturbance Elimination In the study area, there are three main land cover types, which are vegetation, rock and water. The selection of training samples was based on the field survey, remote sensing image, and topographic map. According to the results of the field survey, 52 field rock sampling regions in the image were used as sampling region of rock, and 50–200 pixels were selected as rock samples in each region, which have a total of 6320 rock samples. Then in the vegetation coverage region surrounding the 52 rock sampling regions, and 50–500 pixels were selected as vegetation samples in each region, which have a total of 8460 vegetation samples. Finally, 50–100 pixels of water samples were selected for each of the 50 water regions on the image, which have a total of 4210 water samples. The training samples and reference samples were carried out in ENVI4.8, with 80% of them used as training samples and the remaining 20% as test samples. The details are shown in Table1.

Table 1. Training samples.

Band Type Number of Samples Total Number C1 vegetation 8460 C2 rock 6320 18,990 C3 water 4210

Three bands of remote sensing image were selected, and three kinds of training samples were classified by using two-dimensional feature space. According to Equation (2), the hyperplane equation can be simplified to: d = X cos α + X sin α (12) 2 × 1 × Because a chromosome represents a set of hyperplane sets, and there are three kinds of objects to be classified, a group of hyperplanes has three chromosomes. A hyperplane consists of an angle α and a distance d, and there are 30 chromosomes in the genetic initialization population in this paper. The parameter setting of GA is very important to the result accuracy and time efficiency of hybrid pixel decomposition. In this study, appropriate parameters were determined through the following three tables (Tables2–4).

Table 2. The relationship between variation rate and training accuracy & time.

Variation Rate 0.01 0.02 0.05 0.07 0.09 0.1 0.2 0.3 Generations 1060 1120 2000 2000 1560 2000 2000 2000 Correct Number of 432 456 391 416 381 372 365 371 Classiﬁcations Training Accuracy 0.864 0.912 0.782 0.832 0.762 0.744 0.73 0.742 Training Time (s) 261 259 302 431 462 294 309 192 (Number of samples: 500; Type: 3; Crossing rate: 0.99). Remote Sens. 2019, 11, 3003 9 of 22

When the variation probability is less than 0.1, the training accuracy is high. When the variation probability is set to 0.02, the relationship between the cross rate and the training accuracy and training time is shown in the following table when the other parameters remain unchanged (Table3).

Table 3. The relationship between Crossing rate and training accuracy & time.

Crossing Rate 0.02 0.05 0.1 0.3 0.4 0.6 0.8 0.99 Generations 711 623 658 110 1120 812 421 1128 Correct Number of Classiﬁcations 457 451 451 445 453 472 465 471 Training Accuracy 0.914 0.902 0.902 0.89 0.906 0.944 0.93 0.942 Training Time (s) 149 138 141 92 245 193 121 207 (Number of samples: 500; Type: 3; Variation rate: 0.02).

When the cross rate is 0.3, the training time is the least and the training accuracy is high. When the cross rate is equal to 0.3 and the variation rate is equal to 0.02, the relationship between the number of training points and the training accuracy & time is shown in the following Table4.

Table 4. The relationship between training points and training accuracy & time.

Number of Samples 100 200 300 500 900 1200 2000 4000 Generations 631 292 273 446 68 2000 2000 2000 Correct Number of Classiﬁcations 94 192 273 359 465 864 1187 2739 Training Accuracy 0.94 0.96 0.91 0.718 0.517 0.72 0.594 0.685 Training Time (s) 0.0548 0.089 0.132 0.184 0.231 0.734 0.891 1.632 (Type: 3; Variation rate: 0.02; Crossing rate: 0.3).

When the cross rate is equal to 0.3 and the variation rate is equal to 0.02, the increase of training points will not lead to the increase of training time in a certain range. In this paper, the genetic algorithm uses binary coding. The initial population is randomly selected, and then according to the above research, cross rate is equal to 0.3, and variation rate is equal to 0.02. After 212 generations of genetic evolution, 18,250 training samples were successfully classiﬁed, and the training success rate was 96.1%. The three hyperplane equations and angles acquired by genetic training were as in Table5:

Table 5. Hyperplane set.

Number Distance Angle Hyperplane Equations H1 1.546207 44.136729 1.546207 = x cos 44.136729 + x sin 44.136729 2 × 1 × H2 0.760832 161.892638 0.760832 = x cos 161.892638 + x sin 161.892638 2 × 1 × H3 1.546207 14.834792 1.546207 = x cos 14.834792 + x sin 44.14.834792 2 × 1 ×

The mixed pixel decomposition with hyperplane and GA was used to process the remote sensing images, the categories were determined according to the ﬁnal assignment, the vegetation interference were removed, and results are as in Figure5. Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 23

Table 5. Hyperplane set.

Number Distance Angle Hyperplane Equations

H1 1.546207 44.136729 1.546207 = x2 × cos 44.136729 + x1 × sin 44.136729 H2 0.760832 161.892638 0.760832 = x2 × cos 161. 892638 + x1 × sin 161.892638 H3 1.546207 14.834792 1.546207 = x2 × cos 14.834792 + x1 × sin 44.14.834792 The mixed pixel decomposition with hyperplane and GA was used to process the remote sensing Remote Sens. 2019, 11, 3003 10 of 22 images, the categories were determined according to the final assignment, the vegetation interference were removed, and results are as in Figure 5.

Figure 5. Results of vegetation removal by mixed pixel decomposition decomposition..

Because v vegetationegetation is is the the main main disturbance disturbance factor factor in in extracting extracting mineral mineral alteration alteration information information in ina high a high vegetation vegetation area area,, removing removing or or weakening weakening vegetation vegetation will will greatly greatly improve improve the the accuracy of extracting alteration information. T Thehe s spectralpectral profiles proﬁles of the study image before and after vegetation removal werewere as in Figure6 6..

(a) (b)

Figure 6. 6. TheThe spectral spectral profile proﬁle of the of study the studyimage (a) image before (a) vegetation before vegetation removal (b) removal after vegetation (b) after vegetationremoval. removal.

FigureFigure 66 indicates indicates thethe imageimage hashas basicallybasically no no vegetationvegetation spectralspectral informationinformation afterafter vegetationvegetation removal treatment.treatment. FiguresFigures 55 and and6 6certify certify the the image image of of the the rock rock is is highlighted highlighted in in place place of of the the original original green green part part of of the vegetation cover area after vegetationvegetation removalremoval byby unmixingunmixing withwith hyperplanehyperplane optimizedoptimized byby GA.GA.

4.2. Collection of A Alteredltered M Mineralineral S Samplesamples Through the analysis of the distribution of gold deposits and the ore-bearing strata of the study area, the pyrite, sericite and chlorite are the most closely related to altered mineral of surrounding rock, which is a good prospecting indicator of gold ore [58–60].

The main basis for the selection of bands was based on the characteristic spectrum of minerals to select the two bands with the largest reflectivity difference in ASTER image for ratio operation and to generate the ratio image, which can enhance the spectral difference among minerals and eliminate the interference of topography and shadow. In the light of the minerals reflectance spectra of pyrite, B1, B2, B5/B3, and B4 of ASTER image were selected as input bands for PCA. The eigenvector of principal component was as shown in Table6. Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 23

Through the analysis of the distribution of gold deposits and the ore-bearing strata of the study area, the pyrite, sericite and chlorite are the most closely related to altered mineral of surrounding rock, which is a good prospecting indicator of gold ore [58–60]. The main basis for the selection of bands was based on the characteristic spectrum of minerals to select the two bands with the largest reflectivity difference in ASTER image for ratio operation and to generate the ratio image, which can enhance the spectral difference among minerals and eliminate the interference of topography and shadow. Remote Sens. 2019, 11, 3003 11 of 22 In the light of the minerals reflectance spectra of pyrite, B1, B2, B5/B3, and B4 of ASTER image were selected as input bands for PCA. The eigenvector of principal component was as shown in Table 6. Table 6. Pyrite eigenvector matrix.

Eigenvector B1 B2 B5/B3 B4 Table 6. Pyrite eigenvector matrix. 1 0.166731 0.120621 0.822539 0.530171 2 Eigenvector0.106507 B1 0.233433B2 B5/B30.562449 B4 0.786013 − 3 1 0.5998490.166731 0.7341710.120621 0.822539 0.024860 0.530171 0.317107 − 4 0.775267 0.626062 0.080392 0.023353 2 − 0.106507 0.233433 −0.562449 0.786013 3 0.599849 0.734171 0.024860 −0.317107 According to the spectral4 curve− characteristics0.775267 0.626062 of pyrite, 0.080392 the spectral 0.023353 curve at B1 of ASTER image is absorptionAccording valley, to the and spectral the coe curvefficient characteristics symbol should of pyrite, be negative the spectral in the curve eigenvector at B1 of ASTER matrix. image The spectralis absorption curve valley, at B2and and B4 the of coefficient ASTER image symbol are should reflection be peak,negative and in the the coe eigenvectorfficient symbol matrix. should The bespectral positive curve in theat B eigenvector2 and B4 of ASTER matrix. image The PC4 are inreflection Table6 satisfiespeak, and the the above coefficient conditions, symbol so should the PC4 be reflectspositive the in alterationthe eigenvector information matrix. of The iron PC4 mineralization in Table 6 andsatisfies was the selected above as conditions, the extraction so the layer PC4 of pyritereflects mineralization. the alteration information of iron mineralization and was selected as the extraction layer of pyriteIn mineralization. order to get pyrite mineralization, PC4 needs to be further processed. Firstly, the PC4 was filteredIn byorder using to get 3 pyrite3 median mineralization, filter to eliminate PC4 needs the noise. to be Then, further the processed result of the. Firstly, filter was the stretchedPC4 was × linearlyfiltered by by using using 3 0–255 × 3 median grey value. filter Finally,to eliminate the mineralization the noise. Then, information the result of was the segmented filter was stretched by using thelinearly average by using value 0 plus–255 standardgrey value. deviation Finally, of the 3.0 mineralization times as the threshold, information and wa thes segment segmentationed by resultusing wasthe average as shown value in Figure plus 7standard: deviation of 3.0 times as the threshold, and the segmentation result was as shown in Figure 7:

Figure 7. Alteration distribution of pyrite mineralization.mineralization.

FigureFigure7 7 shows shows that that the the altered altered zone zone of of pyrite pyrite has has obvious obvious ribbon ribbon and and planar planar distribution distribution characteristics,characteristics, which are mainly distributed in the western of the study area, and a smallsmall amountamount scattered inin thethe northeastnortheast ofof thethe studystudy area,area, formingforming aa fourth-stepfourth-step ladderladder spatialspatial distributiondistribution patternpattern along thethe northwest-southeastnorthwest-southeast direction. Along the northwest-southeastnorthwest-southeast directiondirection are the alterationalteration belts formed in the northeast, southeast and southwest of Dacun rock mass; in the east and south of Gulong rock mass; in the surrounding of Pingtoubei, Sitai, Shedong, Dapo rock mass; as well as in the surrounding of Wujie, Ludong, Fenghuang, Fuqing, and Siwei rock mass (Figure7).

In the same way, in the light of the minerals reﬂectance spectra of sericite, B1, B4/B6, B7, and B9 of the ASTER image were selected as input bands for PCA. The eigenvector of principal component was as shown in Table7. Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 23

Gulong rock mass; in the surrounding of Pingtoubei, Sitai, Shedong, Dapo rock mass; as well as in the surrounding of Wujie, Ludong, Fenghuang, Fuqing, and Siwei rock mass (Figure 7). In the same way, in the light of the minerals reflectance spectra of sericite, B1, B4/B6, B7, and B9 Remote Sens. 2019, 11, 3003 12 of 22 of the ASTER image were selected as input bands for PCA. The eigenvector of principal component was as shown in Table 7. Table 7. Sericite eigenvector matrix. Table 7. Sericite eigenvector matrix. Eigenvector B1 B4/B6 B7 B9 1 Eigenvector0.164443 B1 0.811045B4/B6 B7 0.523520 B9 0.202710 − 2 1 0.0703430.164443 0.8110450.569964 0.523520 0.690800 −0.202710 0.439304 − 3 2 0.8593940.070343 − 0.0382990.569964 0.6908000.353052 0.439304 0.367872 − 4 0.479012 0.126017 0.352244 0.794098 3 − 0.859394 0.038299 −0.353052 0.367872 − 4 −0.479012 0.126017 0.352244 −0.794098 According to to the the spectral spectral curve curve characteristics characteristics of sericite, of sericite, the spectral the spectral curve curve at B1 and at B1 B9 and of ASTER B9 of imageASTER are image an absorption are an absorption valley, and the valley, coeffi andcient the symbol coefficient should besymbol negative should in the be eigenvector negative matrix. in the Theeigenvector spectral curve matrix. at B7The of spectral the ASTER curve image at B7 isa of reflection the ASTER peak, image and the is coe a ffireflectioncient symbol peak, should and the be positivecoefficient in symbol the eigenvector should be matrix. positive The in PC4the eigenvector in Table7 satisfied matrix. the The above PC4 in conditions, Table 7 satisfie so thed the PC4 above was selectedconditions, as theso the extraction PC4 wa layers selected of sericite as the mineralization. extraction layer of sericite mineralization. InIn orderorder toto getget sericitesericite mineralization, mineralization the, the PC4 PC4 needed needed to to be be further further processed processed in in the the same same way way as pyrite,as pyrite, and and the the segmentation segmentation result result was wa ass shownas shown in Figurein Figure8: 8:

Figure 8. Alteration distribution of sericite mineralization.mineralization.

FigureFigure8 8indicates indicates the the altered altered zone zone of of sericite sericite has has obvious obvious ribbon ribbon and and planar planar distribution distribution characteristics, which which are are mainly mainly distributed distributed in the in western the western of the study of the area, study forming area, a fourth forming-step a fourth-stepladder spatial ladder distribution spatial distribution pattern along pattern the northwest along the-southeast northwest-southeast direction. Along direction. the northwest Along the- northwest-southeastsoutheast direction are direction the alter areation the belts alteration formed belts in the formed northeast in the of northeast Dacun rock of Dacunmass; in rock the mass; interior in theof Gulong interior rock of Gulong mass; in rock the mass;surrounding in the surrounding of Pingtoubei, of Sitai, Pingtoubei, Shedong, Sitai, Dapo Shedong, rock mass; Dapo as rock well mass; as in asthe well surrounding as in thesurrounding of Wujie, Ludong, of Wujie, Fenghuang, Ludong, Fenghuang,Fuqing, and Fuqing,Siwei rock and mass Siwei (Figure rock mass 8). (Figure8). Similarly,Similarly,in in the the light light of of the the minerals minerals reﬂectance reflectance spectra spectra of chlorite, of chlorite B1, B2,, B1, B5 B2,/B8 andB5/B8 B9 and of ASTER B9 of imageASTER were image selected were selected as input as bands input for bands PCA. for The PCA. eigenvector The eigenvector of the principal of the principal component component was as shown was inas Tableshown8. in Table 8.

Table 8. Chlorite eigenvector matrix. Table 8. Chlorite eigenvector matrix. Eigenvector B1 B2 B5/B8 B9 Eigenvector B1 B2 B5/B8 B9 1 0.162074 0.799186 0.516097 0.262064 1 − −0.162074 0.799186 0.516097 0.262064 2 0.063939 0.592936 0.688779 0.412216 2 0.063939 −−0.592936 0.688779 0.412216 3 0.909014 0.073300 0.309002 0.269885 − − 4 0.378598 0.065995 0.404654 0.829797 − Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 23

Remote Sens. 2019, 11, 3003 13 of 22 3 0.909014 0.073300 −0.309002 −0.269885 4 0.378598 −0.065995 0.404654 0.829797 According to the spectral curve characteristics of c chlorite,hlorite, the spectral curve at B2 of the ASTER image is an absorption valley, and the coefficient coefficient symbol should be negative in the eigenvector matrix. The spectral curve at B1 and B9 of the ASTER image are reflectionreflection peaks,peaks, and the coecoefficientfficient symbol shoulshouldd be positive in the eigenvector matrix. The The PC4 PC4 in in Table Table 88 satisfiessatisfies thethe aboveabove conditions,conditions, soso thethe PC4 is selected as the extraction layer of chloritechlorite mineralization. In order to get c chloritehlorite mineralization, the PC4 needs to be further processed in the sam samee way as pyrite and sericite, and the segmentation result areare asas shownshown inin FigureFigure9 9: :

Figure 9. Alteration distribution of chlorite mineralization mineralization..

FigFigureure9 illustrates9 illustrates that that the alteredthe altered zone of zone chlorite of has chlorite obvious has ribbon obvious distribution ribbon characteristics distribution characteristicsin Figure8, which in Fig areure mainly 8, which distributed are mainly in the distributed western of in the the study west area,ern of and the a smallstudy amountarea, and scattered a small amountin the northeast. scattered Specificallyin the northeast they. are Specifically distributed they in theare Dacundistributed rock in mass; the inDacun the interior rock mass of Gulong; in the interiorrock mass; of Gulong in the surrounding rock mass; in of the Pingtoubei, surrounding Sitai, of Shedong, Pingtoubei Dapo, Sitai, rock Sh mass;edong, as Dapo well asrock in mass Ludong,; as wellFenghuang, as in Ludong, Fuqing, Fenghuang and Siwei, rockFuqing, mass and (Figure Siwei9 ).rock mass (Figure 9). In general, general, the the altered altered mineral mineral formation formation of pyrite, of pyrite, sericite sericite and chlorite and chlorite are closely are closely related related to the hydrothermalto the hydrothermal alteration, alteration, so the so altered the altered zone zone has has the the cha characteristicsracteristics surrounding surrounding rock rock mass distribution [60,61]. In In addition, addition, the the alteration alteration area area is distributed distributed both sides of of the the Limu Limu-Majiang-Majiang fault near the the north-south north-south direction, direction, and and in the in south the south of the of Pingxiang-Dali the Pingxiang fault-Dali near fault the near northeast the northeast direction, direction,so the spatial so the distribution spatial distribution of the altered of the zone altered is closely zone related is closely to the related intrusive to the rocks intrusive and faults. rocks and faults. 4.3. Altered Mineral Information Extraction 4.3. AlteredIn order M toineral obtain Information a mineralization Extraction alteration sample, the mineralization alteration information of pyrite,In sericite,order to and obtain chlorite a mineralization were loaded alteration into the previous sample, surveythe mineralization results map (Figurealteration 10 ).information of pyrite,The selectionsericite, and of trainingchlorite were samples loaded was into based the previous on the field survey survey, results remote map sensing(Figure 10 image). and topographicThe selection map. of According training samples to the results was based of the on field the survey field survey, and 14 goldremote deposits sensing in ima Figurege and 10, topographic300 pixels each map. altered According minerals to the were results selected of the from field the survey image and where 14 gold 14 gold deposits mining in Figure areas overlap10, 300 withpixels altered each altered minerals. minerals At the were same selected time, 900from pixels the image were where selected 14 ingold the mining non-mining areas overlap area and with the alterednon-alteration minerals. mineral At the area, same with time, a total900 pixels of 1800 we pixelsre selected as the in training the non and-mining test samples area and to trainthe non the- alterationSVM model. mineral Among area, them, with 80% a total of the of samples1800 pixels (half as of the them training were mineralizationand test sample alteration,s to train 1the/3 ofSVM per model.mineralization Among alteration)them, 80% were of the randomly samples selected (half of asthem training were samples, mineralization and the alteration, remaining 1/3 20% of were per mineralizatiused as the teston alteration) samples. were randomly selected as training samples, and the remaining 20% were used as the test samples.

Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 23

Remote Sens. 2019, 11, 3003 14 of 22 Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 23

Figure 10. Distribution map of gold deposits in Dayaoshan area (Remote Sensing Center of Guangxi).

On the Figurebasis 10.of Distributionselecting map SVM of gold kernel deposits function, in Dayaoshan the SVM area (Remote was trained Sensing Center by the of Guangxi).Guangxi)above .sample data, and at the sameOn thethe time, basisbasis of ofthe selecting selecting SVM SVM SVMwa kernels kernel optimized function, function, theby the SVMthe SVM wasACA, wa traineds trained in whichby theby the above the above sample maximum sample data, data, and number of iterationsatand theK at= same 10, the ant sametime, colony the time, SVM the sizewas SVM N optimized wa= 20,s optimized pheromone by the ACA, by the evaporation in ACA, which in the which maximum coefficient the maximum number Rho of = numberiterations 0.8, pheromone of increasingKiterations= intensity10, ant K colony = Q10, = ant size0.9, colony Nant= 20, crawling size pheromone N = 20, speed pheromone evaporation Lambda evaporation coe =ﬃ 0.3.cient Rhocoefficient= 0.8, pheromoneRho = 0.8, pheromone increasing In orderintensityincreasing to Qverify intensity= 0.9, the ant Q crawlingcorrectness = 0.9, ant speed crawling of Lambda the speed SVM= Lambda0.3. model =, 0.3.the area under curve (AUC) of the receiver In order to verify the correctness of the SVM model, the area under curve (AUC) of the receiver operating characteristicIn order to verify (ROC the )correctness curve wa ofs introducedthe SVM model in, thisthe a rearesearch under c(Figurveure (AUC 11) ).of Fig theure receiver 11 indicate s operatingoperating characteristiccharacteristic (ROC)(ROC) curve waswas introduced in thisthis researchresearch (Figure(Figure 1111).). FigureFigure 1111 indicatesindicates that the thatthatAUC the was AUC AUC 0. was was889 0. 0.889and889 andthe and theforecast the forecast forecast accuracy accuracy accuracy was was 88.9 88.9 88.9%.%.%. This This result resultresult indicate indicates indicates that that thes that theSVM SVMthe has SVM has higher predictionhashigher higher prediction predictionaccuracy. accuracy. accuracy. Therefore, Therefore, Therefore, the the model model the model built in builtin this this in paper thispaper papercan canbe canused be be usedto used extract to to extract extractthe alter thetheed altered mineral.alteredmineral mineral..

Figure 11. Receiver operating characteristic (ROC) curve of the SVM model.

The altered mineral obtained using the PCA method in the study area were input into the trained Figure 11. Receiver operating characteristic (ROC) curve of the SVM model. SVM, the Figurefalse information 11. Receiver was o removed,perating candharacteristic the mineralization (ROC) curve alteration of the results SVM w modelere obtained. , as shownThe in altered Figure mineral12. obtained using the PCA method in the study area were input into the trained TheSVM, alter theed mineral false information obtained was using removed, the PCA and themethod mineralization in the study alteration area results were input were obtained, into the trained SVM, theas false shown information in Figure 12. was removed, and the mineralization alteration results were obtained, as shown in Figure 12.

Remote Sens. 2019, 11, 3003 15 of 22 Remote Sens. 2019, 11, x FOR PEER REVIEW 15 of 23

Figure 12. Extraction results of alter altereded mineral information.information. Figure 12 indicates that the mineral alteration zone has obvious ribbon and planar distribution Figure 12 indicates that the mineral alteration zone has obvious ribbon and planar distribution characteristics, and forming three regions in space: located in the area of Dacun and Gulong rock mass; characteristics, and forming three regions in space: located in the area of Dacun and Gulong rock located surrounding Pingtoubei, Sitai, Shedong, and Dapo rock mass, as well as located surrounding mass; located surrounding Pingtoubei, Sitai, Shedong, and Dapo rock mass, as well as located Fenghuang, Fuqing, and Siwei rock mass. Compared with Figures7–9, it can be seen that the range surrounding Fenghuang, Fuqing, and Siwei rock mass. Compared with Figures 7–9, it can be seen of the alteration area is smaller, which is due to the removal of some false anomalies. Figure 12 also that the range of the alteration area is smaller, which is due to the removal of some false anomalies. indicates that mineral alteration does not exist alone, but is associated with each other, which is a good Figure 12 also indicates that mineral alteration does not exist alone, but is associated with each other, prospecting indicator for gold deposits. which is a good prospecting indicator for gold deposits. In summary, there are obvious reflection characteristics near 0.67 µm and an absorption valley In summary, there are obvious reflection characteristics near 0.67 μm and an absorption valley near 0.81 µm for pyrite, and absorption characteristics at 2.17 µm for sericite, as well as absorption near 0.81 μm for pyrite, and absorption characteristics at 2.17 μm for sericite, as well as absorption characteristics at 2.35 µm for chlorite. Those characteristics in combination can be used to identify the characteristics at 2.35 μm for chlorite. Those characteristics in combination can be used to identify the alteration zone and support mineral prediction. At present, many scholars have used multi-spectral alteration zone and support mineral prediction. At present, many scholars have used multi-spectral data to identify the alteration zone based on above combination of characteristics [60,62,63]. data to identify the alteration zone based on above combination of characteristics [60,62,63]. 4.4. Verification of Altered Mineral Information Extraction Results 4.4. Verification of Altered Mineral Information Extraction Results In order to verify the accuracy of the mineral alteration information extracted, a special field In order to verify the accuracy of the mineral alteration information extracted, a special field investigation was carried out. First of all, the working scope of field verification was determined investigation was carried out. First of all, the working scope of field verification was determined according to the spatial distribution of remote sensing alteration information. Then, the field survey according to the spatial distribution of remote sensing alteration information. Then, the field survey route was planned according to the regional geological characteristics. Finally, the rock specimens route was planned according to the regional geological characteristics. Finally, the rock specimens with alteration anomalies were collected in different locations. After 15 days of field sampling and with alteration anomalies were collected in different locations. After 15 days of field sampling and verification in Gulong of Dayao Mountain, 116 rock samples were collected at 52 sampling points verification in Gulong of Dayao Mountain, 116 rock samples were collected at 52 sampling points (Figure 13), and the rock sample verification analysis was as follows (Figures 14–16). (Figure 13), and the rock sample verification analysis was as follows (Figures 14–16).

Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 23

Remote Sens. 2019, 11, 3003 16 of 22 Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 23

Figure 13. Spatial distribution of 52 field sampling points.

(1) The exposed rocks in Sanxianding have obvious pyritization with a light brass color and bright metallic luster, which shows that the altered minerals contained in the outcrop rocks in the field are consistent with the results extracted using ASTER images (Figure 14). In hydrothermal deposits, pyrite is symbiotic with other sulfides, oxides, quartz and so on. This phenomenon indicates that such rock compositionFigure often 13.13. contains SpatialSpatial distribution gold, silver of 52and ﬁeldfield other sampling elements. points.points .

(a) (b)

RemoteFigure Sens. 2019 14., SamplesS11amples, x FOR atatPEER SanxiandingSanxianding REVIEW (110.945 (110.945°E,◦E, 23.641 23.641°N;◦N; 7-2-2015) 7-2-2015 ()a ()a View) View of of the the surrounding surrounding rock rock for17 of 23 pyritization,for pyritization (b), The(b) The rock rock sample sample of the of pyritization.the pyritization.

(2) The rocks in Hecun sampling area are mainly granite porphyry with 15-20% phenocryst, the main rock components are quartz and feldspar, and sometimes biotite and hornblende. Quartz phenocrysts usually to be hexagonal(a) bipyramid, and biotite and hornblende(b) sometimes were existed darkening edges (Figure 12). This phenomenon indicates that there are intrusive rocks in the Figure 14. Samples at Sanxianding (110.945°E, 23.641°N; 7-2-2015) (a) View of the surrounding rock samplingfor pyritization area, which, (b )directly The rock proves sample the of the existence pyritization of mineral. alteration in surrounding rock (Figure 15). Metallic minerals related to granitic porphyry are gold, silver, copper, and so on. (2) The rocks in Hecun sampling area are mainly granite porphyry with 15-20% phenocryst, the main rock components are quartz and feldspar, and sometimes biotite and hornblende. Quartz phenocrysts usually to be hexagonal bipyramid, and biotite and hornblende sometimes were existed darkening edges (Figure 12().a ) This phenomenon indicates that there(b) are intrusive rocks in the sampling area, which directly proves the existence of mineral alteration in surrounding rock (Figure Figure 15. SSamplesamples at at Hecun Hecun (111.027°E, (111.027◦E, 23. 23.540540°N;◦N; 8 8-2-2015)-2-2015) ( (aa)) View View of of the the g graniticranitic porphyry porphyry,, ( b) The 15). Metallicrock sample minerals of the graniticgrelatedranitic porphyry.porphyryto granitic. porphyry are gold, silver, copper, and so on.

(3) The sampling point is mainly meso-basic igneous rock and metasomatic rock, which is the surrounding rock related to chloritization (Figure 16). Chloritization rarely occurs alone, often accompanied by pyritized, sericitization, propylitization, and carbonate and so on.

(a) (b)

Figure 16. Samples at Sunjialing (111.013°E, 23.652°N; 10-2-2015) (a) View of the surrounding rock related to chloritization, (b) The rock sample of the surrounding rock related to chloritization.

The results of field verification certified that at 40 of the 52 sampling sites rocks containing pyrite, sericite and chlorite can be collected, which is consistent with the results of mineralization and alteration extraction using remote sensing image. In order to further verify the reliability of the proposed method, the spectral curves of rock samples collected in the field were measured and analyzed in laboratory, and the results were as shown in Figure 17.

Remote Sens. 2019, 11, x FOR PEER REVIEW 17 of 23

(a) (b)

Figure 15. Samples at Hecun (111.027°E, 23.540°N; 8-2-2015) (a) View of the granitic porphyry, (b) The rock sample of the granitic porphyry.

(3) The sampling point is mainly meso-basic igneous rock and metasomatic rock, which is the surroundingRemote Sens. 2019 rock, 11, 3003 related to chloritization (Figure 16). Chloritization rarely occurs alone, 17 often of 22 accompanied by pyritized, sericitization, propylitization, and carbonate and so on.

(a) (b)

FiguFigurere 16. SSamplesamples at Sunjia Sunjialingling (111.013°E, (111.013◦E, 23.652°N; 23.652◦N; 10 10-2-2015)-2-2015) ( (aa)) View View of of the the surrounding rock related to c chloritization,hloritization, (b) The rock sample of the surrounding rockrock relatedrelated toto chloritization.chloritization.

T(1)he The results exposed of field rocks verification in Sanxianding certified have that obvious at 40 of pyritizationthe 52 sampling with sites a light rocks brass containing color and pyrite, bright sericitemetallic and luster, chlorite which can shows be collectedthat the altered, which minerals is consistent contained with in the the results outcrop of rocks mineralization in the field and are alterationconsistent extraction with the results using extractedremote sensing using ASTER image. images (Figure 14). In hydrothermal deposits, pyrite is symbioticIn order with to further other sulfides, verify the oxides, reliability quartz of and the so proposed on. This phenomenon method, the spectral indicates curves that such of rock rock samplecompositions collected often in contains the field gold, were silver measured and other and elements. analyzed in laboratory, and the results were as shown(2) in The Fig rocksure 17 in. Hecun sampling area are mainly granite porphyry with 15–20% phenocryst, the main rock components are quartz and feldspar, and sometimes biotite and hornblende. Quartz phenocrysts usually to be hexagonal bipyramid, and biotite and hornblende sometimes were existed darkening edges (Figure 12). This phenomenon indicates that there are intrusive rocks in the sampling area, which directly proves the existence of mineral alteration in surrounding rock (Figure 15). Metallic minerals related to granitic porphyry are gold, silver, copper, and so on. (3) The sampling point is mainly meso-basic igneous rock and metasomatic rock, which is the surrounding rock related to chloritization (Figure 16). Chloritization rarely occurs alone, often accompanied by pyritized, sericitization, propylitization, and carbonate and so on. The results of field verification certified that at 40 of the 52 sampling sites rocks containing pyrite, sericite and chlorite can be collected, which is consistent with the results of mineralization and alteration extraction using remote sensing image. In order to further verify the reliability of the proposed method, the spectral curves of rock samples collected in the field were measured and analyzed in laboratory, and the results were as shown in Figure 17. By comparing spectral curves between Figure 17 and Spectroscopy Lab of the United States Geological Survey (USGS) [64], the results show that the rock samples collected in the field have altered mineral characteristic of pyrite, sericite, and chlorite, respectively, which are the main signs for gold prospecting. The results indicate that the extraction results of alteration information in this paper have very high accuracy and could play a vital role in the exploration of metal minerals in the study area. According to the extracted mineralization alteration and the results of field verification, the favorable areas of gold deposit can be delineated (Figure 18). Remote Sens. 2019, 11, 3003 18 of 22 Remote Sens. 2019, 11, x FOR PEER REVIEW 18 of 23

(a)

(b)

(c)

FigureFigure 17. The 17. spectralThe spectral curves curves of ofsample sampless collected collected inin thethe ﬁeld field (a -Feldspar(a-Feldspar quartz quartz sandstone sandstone from from Remote Sens. 2019, 11, x FOR PEER REVIEW 19 of 23 SanxiandingSanxianding),), (b-Q (uartzb-Quartz porphyry porphyry from from Sunjialing Sunjialing),), ((cc-Granite-porphyry-Granite-porphyry from from Hecun). Hecun).

By comparing spectral curves between Figure 17 and Spectroscopy Lab of the United States Geological Survey (USGS) [64], the results show that the rock samples collected in the field have altered mineral characteristic of pyrite, sericite, and chlorite, respectively, which are the main signs for gold prospecting. The results indicate that the extraction results of alteration information in this paper have very high accuracy and could play a vital role in the exploration of metal minerals in the study area. According to the extracted mineralization alteration and the results of field verification, the favorable areas of gold deposit can be delineated (Figure 18).

Figure 18. TheThe favorable areas of gold deposit.deposit.

5. Conclusions This study describes a methodology for gold ore deposit identification based on ASTER data using SVM and PCA in areas of high vegetation. Gulong in the Dayaoshan metallogenic belt of Guangxi was taken as the study area for field verification. The results show that the mineral alteration regions of the Gulong can be mapped well with field investigations. At the same time, the results also show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing, especially in areas with high vegetation cover. This study results in the following three main conclusions: (1) Altered mineral is the weak information in the image, which is very vulnerable to inundation in the background. At the same time, the study area is located in the gold ore-forming zone of the southern margin of the Dayaoshan-Daguishan, where exposed intrusive rocks and surrounding rock alteration can be found on the surface by field exploration. That altered mineral information can be disturbed by high coverage of vegetation. The mixed pixel decomposition method with hyperplane optimized using the genetic algorithm is used to remove the influence of vegetation interference information and highlight altered mineral [65] (Figure 5). Thus, it provides a methodological reference for areas with high vegetation coverage and strong disturbance information. (2) The extraction model of altered mineral information was constructed based on PCA and SVM optimized by ACA [66], and successfully extracted the altered mineral in the study area, by comprehensively processing the multi-source heterogeneous data, such as remote sensing ground object spectral data, laboratory mineral spectrum data, rock mineral distribution data, and so on. On this basis, the favorable metallogenic areas were mapped in the study area (Figure 18). (3) By comparing spectral curves between field rock samples and Spectroscopy Lab of USGS [64], the results show that the rock samples collected in the field have altered mineral characteristic of pyrite, sericite, and chlorite, respectively, which are the main signs for gold prospecting. Simultaneously, field investigations verified that the distribution of extracting alteration areas from ASTER display a good correlation with the alteration regions, which further demonstrates that the method of extracting alteration mineralization information adopted in the study is feasible. In general, the results can provide technical support for multi-source heterogeneous data fusion and geological big data metallogenic prediction, especially in areas of high vegetation cover.

Remote Sens. 2019, 11, 3003 19 of 22

5. Conclusions This study describes a methodology for gold ore deposit identification based on ASTER data using SVM and PCA in areas of high vegetation. Gulong in the Dayaoshan metallogenic belt of Guangxi was taken as the study area for field verification. The results show that the mineral alteration regions of the Gulong can be mapped well with field investigations. At the same time, the results also show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing, especially in areas with high vegetation cover. This study results in the following three main conclusions:

(1) Altered mineral is the weak information in the image, which is very vulnerable to inundation in the background. At the same time, the study area is located in the gold ore-forming zone of the southern margin of the Dayaoshan-Daguishan, where exposed intrusive rocks and surrounding rock alteration can be found on the surface by field exploration. That altered mineral information can be disturbed by high coverage of vegetation. The mixed pixel decomposition method with hyperplane optimized using the genetic algorithm is used to remove the influence of vegetation interference information and highlight altered mineral [65] (Figure5). Thus, it provides a methodological reference for areas with high vegetation coverage and strong disturbance information. (2) The extraction model of altered mineral information was constructed based on PCA and SVM optimized by ACA [66], and successfully extracted the altered mineral in the study area, by comprehensively processing the multi-source heterogeneous data, such as remote sensing ground object spectral data, laboratory mineral spectrum data, rock mineral distribution data, and so on. On this basis, the favorable metallogenic areas were mapped in the study area (Figure 18). (3) By comparing spectral curves between field rock samples and Spectroscopy Lab of USGS [64], the results show that the rock samples collected in the field have altered mineral characteristic of pyrite, sericite, and chlorite, respectively, which are the main signs for gold prospecting. Simultaneously, field investigations verified that the distribution of extracting alteration areas from ASTER display a good correlation with the alteration regions, which further demonstrates that the method of extracting alteration mineralization information adopted in the study is feasible.

In general, the results can provide technical support for multi-source heterogeneous data fusion and geological big data metallogenic prediction, especially in areas of high vegetation cover.

Author Contributions: All the authors made significant contributions to the work. K.X., X.W., R.F., and C.K. designed the research, analyzed the results, and accomplished the validation work. K.X., and X.W. completed field investigation, sampling, analysis, and validation; G.L., and C.W. provided advice for the revision of the paper. Funding: This research received no external funding. Acknowledgments: The authors would like to thank the Remote Sensing Center of Guangxi for providing the various data sets used in this paper. This work has been supported by the National Natural Science Foundation of China (No: 41201193); the Research on evaluation method of Oil and Gas Resources investigation (prediction) in “Belt and Road Initiative” Spatial Information Corridor; Guizhou Science and Technology Planning Project: Research and development application of big data Management and Intelligent processing system for Mine Exploration ([2017]2951); the Open research project of key laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education (No: TPR-2019-11); and the Open fund project of National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns (CTCZ19K01). The authors would like to thank the anonymous reviewers for providing valuable comments on the manuscript. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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