An Unsupervised Change Detection Method Using Time-Series of Polsar Images from Radarsat-2 and Gaofen-3

sensors

Article An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3

Wensong Liu, Jie Yang *, Jinqi Zhao ID , Hongtao Shi and Le Yang

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; [email protected] (W.L.); [email protected] (J.Z.); [email protected] (H.S.); [email protected] (L.Y.) * Correspondence: [email protected]; Tel.: +86-139-7151-2278

Received: 31 December 2017; Accepted: 6 February 2018; Published: 12 February 2018

Abstract: The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

Keywords: time-series; unsupervised change detection; PolSAR; omnibus test statistic; GSRM; GGMM

1. Introduction The successful launch of China’s first multi-polarization synthetic aperture radar (SAR) imaging satellite (Gaofen-3, GF3) on 10 August 2016 [1], has greatly promoted research on PolSAR in China [2]. The GF3 data possess not only the advantages of the traditional SAR images, such as being immune to the influence of weather and illumination, but they also feature a variety of polarization imaging modes, allowing us to obtain more information on the scattering of objects and achieve improved object interpretation [3]. With the development of PolSAR satellites, a large number of time-series PolSAR images are now available from different sensors (such as ENVISAT-ASAR, ALOS-PALSAR, TerraSAR-X, Radarsat-2), which can better reflect the dynamic changes of the Earth’s surface. These images have been used in a wide range of applications in the fields of disaster prevention and mitigation [4–6], agriculture monitoring [7], forestry [8], land-cover change [9,10] and weather forecasting [11]. Therefore, research on change detection with PolSAR time-series images is of great significance and has aroused widespread interest [12–14]. Although the traditional pixel-based unsupervised change detection methods for PolSAR images can detect the change between two different times with the same sensor (see Table1 for a summary of advantages and disadvantages using traditional unsupervised change-detection methods [15,16]), they cannot detect the total change of a time series from different sensors [17]. To solve these problems, some researchers have simply compared pair-wise images in the time-series images and detected

Sensors 2018, 18, 559; doi:10.3390/s18020559 www.mdpi.com/journal/sensors Sensors 2018, 18, 559 2 of 19 the time-series change, which suffers from some deﬁciencies, i.e., it is time-consuming and cannot detect some small, continuous changes [17]. On the other hand, the results of traditional pixel-based unsupervised change detection methods are susceptible to speckle noise [18], which can result in a high false alarm rate. In recent years, to address these issue, researchers have proposed object-oriented change detection methods that use a segmentation algorithm to segment the PolSAR images from two different times, and then used the traditional pixel-based method to detect the change [19,20]. Although these methods can reduce the effect of speckle noise, they cannot maintain the consistency of segmentation. When analyzing the difference images, traditional methods such as Two-Dimensional Entropic Segmentation (TDES) algorithm [21], Otsu’s thresholding algorithm [22], improved Kittler and Illingworth (K&I) algorithm [23] and Kapur’s entropy algorithm [24], assume that the Probability Density Function (PDF) of the difference image complies with a Gaussian distribution. However, the difference images calculated by omnibus test statistic and Rj test statistic algorithm do not comply with a Gaussian distribution [17]. As a consequence, traditional algorithms are not suitable for the analysis of difference image which obtained by omnibus or Rj test statistic algorithms. Fortunately, Gaussian Mixture Models (GMM) can ﬁt any distribution of data [25]. In our previous work [26], by improving the GMM algorithm, we are better able to analyze image differences.

Table 1. A summary of the traditional unsupervised change detection methods.

Methods Relative Algorithms Advantages Disadvantages Simple and easy to Difficult to select the optimal Algebraic Image ratioing; Log-ratio operator; interpret change threshold and easy to lose Operation Regression analysis detection results. change information. Vegetation index Can reduce the data Strictly require the remotely differencing(VID); Change vector redundancy and also sensed data acquired from the Transformation analysis (CVA); Principal emphasize the different same phenological period and component analysis (PCA); information of the difficult to select the optimal Tasselled cap transformation (KT) derived components. threshold. Some of the traditional Allow straight forward segmentation algorithms Object-based comparison of objects cannot maintain the Direct object-basedcomparison change detection and reduce the influence consistency of PolSAR images of speckle noise. and might cause higher false alarm rates. Simple and can be Cannot be applied in Hidden Markov chain model applied in multi-temporal PolSAR Other methods (HMM); Kullback-Leibler multi-temporal change detection and the divergence and so on. single-channel SAR selection of thresholding based change detection. on a Gaussian distribution.

In this paper, we expand on our previous work in time-series analysis and present an unsupervised change detection method (named OT_GSRM_GGMM) for different sensors. Firstly, the overall difference image of the time-series PolSAR images is calculated by omnibus test statistics, and the difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a GSRM algorithm which can suppress the effect of speckle noise. GGMM is used to obtain time-series change detection maps in the last step for the proposed method. Since the city of Wuhan in China has undergone tremendous changes during the ‘Twelfth Five-Year Plan‘ period [27], the proposed method of this paper was used to detect the changes in Wuhan’s urban development from 2011 to 2017. This paper is organized as follows: in Section2, the framework of time-series change detection is described and the methods of omnibus test statistic, GSRM and GGMM techniques are introduced. Section3 details the results of the proposed approach on time-series PolSAR images from the city of Wuhan. Section4 discusses the results of the case study. Finally, the conclusions are drawn in Section5. Sensors 2018, 18, 559 3 of 19

2. Materials and Methods

2.1. Omnibus Test Statistic The omnibus test statistic algorithm effectively utilizes polarimetric and temporal information from time-series PolSAR images. A PolSAR image includes the backscattering coefﬁcients of the four polarimetric channels of the object [28]. For orthogonal polarization basis, the scattering information of ground objects can be represented by the following covariance matrix C:

 2 ∗ ∗  * |Shh| ShhShv ShhSvv + =  ∗ | |2 ∗  C  ShvShh Shv ShvSvv  (1) ∗ ∗ 2 SvvShh SvvShv |Svv|

Different elements in covariance matrix C represent the backscattering coefﬁcients. For the multi-look conditions, the covariance matrix C of a PolSAR image obeys the complex Wishart distribution (X ∈ W(p, n, ΣX)) and PDF of C can be described as follows:

1 1 n−p n −1 o f (C) = n |C| exp −tr[Σ C] Γn(p) |ΣC| C p (2) p(p−1)/2 Γp(n) = π ∏ Γ(n − j + 1) j=1 where, tr(·) is the trace of covariance matrix C, n is the number of looks of PolSAR image, and p represents the dimension of matrix C. For fully a PolSAR image, p is equals to 3 [17]. t < t < We assume that the multi-parameters ∑X1 , ∑X2 , ... ∑Xj−1 , ∑Xj , ... ∑Xk of time-series ( 1 2 ... < tk) PolSAR images are independent, and they obey the complex Wishart distribution:

∈ ( ) X1 W p, n1, ΣX1 X ∈ W(p, n , Σ ) 2 2 X2 (3) ··· ∈ ( ) Xk W p, nk, ΣXk where p represents the dimension of X1, X2, ... , Xk, n1, n2, ... , nk is the number of look of

X1, X2,..., Xk, and ΣX1 , ΣX2 ,..., ΣXk represent the scattering matrix of X1, X2,..., Xk. According to omnibus test statistic theory, the H0 hypothesis can be described as H = = = = = 0 : ∑X1 ∑X2 ... ∑Xj−1 ∑Xj ... ∑Xk , which means the matrices of time-series PolSAR images are equal. In other words, if H0 hypothesis were the case, the feature has not changed in the time interval [t1, tk]. If not, the feature has at least one change in the time-series [t1, tk] of PolSAR images. We supposing that the omnibus test statistic based on maximum likelihood estimation (MLE) has f ( ) a joint density ∑X1 , ∑X2 ... ∑Xk , θ , where θ is the set of parameters of the probability function that has generated the data. H0 states that θ ∈ H0, and the likelihood ratio of the omnibus test statistic is as follows: maxθ∈H L(θ) L(∑ ) Q = 0 = S (4) max L(θ) i=k θ∈Ω ( ) ∏ LX1 ΣXi i=1 Sensors 2018, 18, 559 4 of 19 where: i=k i=k i=k i=k 1 −ni ni−p −1 LX (ΣX ) = ΣX ΣX exp{−tr( Σ Xi)} ∏ i i i=k ∏ i ∏ i ∑ Xi i=1 i=1 i=1 i=1 ∏ Γp(ni) i=1 i=k (5) − n i=k 1 ∑ k n −p − L(Σ ) = |Σ| i=1 Σ i exp{−tr(Σ 1|X|)} S i=k ∏ Xi i=1 ∏ Γp(ni) i=1

If n1 = n2 = ··· = nk = n, this leads to the desired likelihood-ratio omnibus test statistic [29]:

 i=k  n    ∏ |Xi|  maxθ∈H L(θ) L(∑ )  =  Q = 0 = S = kpk i 1 (6) = k maxθ∈Ω L(θ) i k |X| L (Σ )   ∏ X1 Xi   i=1

i=k where, X = ∑ Xi, Xi = n < C >i and Equation (6) in logarithmic form is as follows: i=1

i=k lnQ = n{pklnk + ∑ ln|Xi| − kln|X|} (7) i=1

In general, the overall similarity of time-series PolSAR images is measured by −lnQ. The larger the value, the greater the probability that change will occur in time-series PolSAR images.

2.2. Rj Test Statistic The omnibus test statistic algorithm can be used to detect the overall change of the time-series PolSAR images, but it is limited to detecting the change between any two different times. To compensate for the shortcoming of omnibus test statistic, Conradsen et al. formulated the Rj test statistic algorithm, which is used to generate the different images in different time intervals [17]. According to Rj test statistics, if the matrices of any two different PolSAR images in time are equal H = t t ( 0 : ∑Xj−1 ∑Xj ), it indicates that there is no change in the time interval [ j−1, j]. Instead, if the H 6= matrices are not equal ( 1 : ∑Xj−1 ∑Xj ), the change happens between the two images. According to Rj test statistics, the likelihood ratio of the statistic can be shown as follows:

n (j−1)n n ( (j−1) ) jpn jp j |X1+...+Xj−1| |Xj| j |X1+...Xj−1| Rj = (j−1)pn jn = (j−1)p j (8) (j−1) |X1+...+Xj| (j−1) |X1+...Xj|

Equation (8) in logarithmic form is as follows:

j−1 j

lnRj = n{p(jlnj − (j − 1)ln(j − 1)) + (j − 1)ln ∑ Xi + ln Xj − jln ∑ Xi (9) i=1 i=1

Similarly, the similarity of PolSAR images from two any different times is measured by −lnRj. The larger the value, the greater the probability that change will occur between the two images.

2.3. Generalized Statistical Region Merging (GSRM) PolSAR data are often seriously affected by multiplicative speckle noise. Even with ﬁlter processing, the results obtained with traditional pixel-based change detection methods are still affected by speckle noise. Thus, object-oriented change detection algorithm was proposed to suppress the inﬂuence of speckle noise [29] and its core is segmentation. Sensors 2018, 18, 559 5 of 19

The Statistical Region Merging (SRM) algorithm, with its small computation burden, is independent of the statistical characteristics of data. What is more, it has a strong capability of speckle noise immunity, when compared with the super-pixel segmentation algorithm, such as Ncut segmentation [30] and Mean Shift algorithm [31]. However, SRM was originally used in optical image processing with a range of [0, 255], and it cannot be used to segment PolSAR images whose numerical range was not ﬁxed. Fortunately, Lang et al. extended SRM and proposed the generalized SRM (GSRM) algorithm [32]. GSRM algorithm deﬁnes two necessary elements: the merging criteria and merging order. Merging criteria indicates that if two adjacent regions in PolSAR image meet a certain condition, the two regions are merged. According to the martingale theory in probability theory, for two adjacent regions (R, R0) in PolSAR image I, there is:

 v  u  2 0  u 2 2 E R 0 0 u B E R 2 Pr R − R − E R − R ≥  +  ln  ≤ δ (10)  t 2Q |R| |R0e| δ  where 0 < δ ≤ 1. The two adjacent regions (R, R0) are merged under condition of Equation (11):

0 0 R − R ≤ b(R, R ) (11) where: s 1 1 1 2 b(R, R0) = g + ln (12) 2Q |R| |R0| δ Merging order defines which two areas should be merged first when merging PolSAR images. GSRM algorithm adopts a pre-sorting strategy, where the adjacent pairs of pixels in the PolSAR image I are first sorted according to the gradient function f (p, p0). Gradient function defined is as follows:

0 0 p − p f (p, p ) = k 0 k (13) p + p 1

2.4. Generalized Gaussian Mixture Model (GGMM) GMM [25] is an algorithm that can ﬁt any distribution of PDF. Therefore, it is widely used in remote sensing data interpretation, such as clustering and unsupervised change detection. Since the distribution of difference images obtained by GSRM is unknown, the GMM algorithm is more suitable for the analysis of difference images than the traditional algorithms, such as TDES, K&I and Otsu’s thresholding algorithm. Assuming that the number of Gaussian functions of GMM model is k and its expression can be given by Equation (14):

k f (x) = ∑ αi p(x|θi) i=1 (14) n o = 2 2 θ µ1, .., µm, σ1 ,..., σm

2 where αi is the weight of ith Gaussian function, total value of αi equals 1, µi and σi represent the mean and variance of ith Gaussian function, respectively. In the process of analysis, difference image can be described as sum of change class ( p(x|θc) ) and no-change class ( p(x|θu) ) as shown in Equation (15). Notably, p(x|θu) and p(x|θc) have different weights:

K ∑ p(θu)p(x|θu) + ∑ p(θc)p(x|θc) p(xd) = p(k)p(xd|k) = (15) ∑ ∀θu∈Mu ∀θc∈Mc k=1 Sensors 2018, 18, 559 6 of 19

where, p(θu) and p(θc) represent the weight of p(x|θc) and p(x|θu) , respectively, p(x|θu) and p(x|θc) are the PDF of change class and no-change class. In general, the parameters of Equation (15) can be solved by log-likelihood function. However, the existence of unknown parameter θ makes this impossible. The Expectation Maximization (EM) algorithm is a better solution to the model with unknown parameters in GMM [33]. The main idea of EM is to use an iterative method to calculate the weight, mean and variance of all Gaussian functions in GMM. To calculate the mean and variance of each Gaussian function, MLE is the best choice. EM method mainly contains two steps, one is expectation 2 (E) step and another is maximization (M) step. E-step can estimate the parameters of αi, µi and σi by iteratively operating with likelihood function (16):

Q(θ, θˆ(t)) ≡ E[log p(X, γ|θ )|X, θˆ(t)] = log p(X, γˆ|θ ) (16) where: ˆ αˆ m(t)p(xi θm (t)) γˆim = E[γim x, θˆ(t) ] = (17) k ∑ αˆ j(t)p(xi θˆj (t)) j=1 M-step obtains the desired maximum with the derivation operation (Equation (18)) and parameters from E-step: ∂(Q(θ, θˆ(t))) = 0 (18) ∂θ Finally, according to the convergence condition of EM algorithm, the iterative updating of each unknown parameter is realized:

n αi = ∑ γîm/n i=1 n n µi = ∑ γîmxi/∑ γîm (19) i=1 i=1 n n 2 T σi = ∑ γîm(xi − µi) (xi − µi)/d∑ γîm i=1 i=1

Nevertheless, the parameter k (the number of Gaussian function) in traditional GMM algorithm is unknown. Researchers can only estimate the value of k using empirical parameter values or by iterative operating, which is time-consuming and gives unsatisfactory results [25]. Therefore, in our previous study [26], we improved the traditional GMM model and used the elbow method to obtain the optimal value of k in advance, which can solve the problem of the traditional GMM algorithm, where parameter k cannot be determined.

2.5. Quantitative Evaluation Criteria It is very important to evaluate the results of proposed method using quantitative evaluation criteria. The False Alarm (FA) rate, Omission Factor (OF) rate, Overall Accuracy (OA) and Kappa coefﬁcient (Kappa)[23] of the experimental results were calculated in this study and the quantitative evaluation criteria are calculated as follows:

 FA = FP  Nu  FN  OF =  Nc  TP+TN OA = N (20)  −  Kappa = OA Pe  1−Pe   = (TP+FN)(TP+FP)+(FP+TN)(FN+TN) Pe N2 Sensors 2018, 18, 559 7 of 19 where, FP means the number of unchanged points incorrectly detected as changed; FN means the number of changed points incorrectly detected as unchanged; TP means the number of changed points correctly detected; TN means the number of unchanged points correctly detected; Nu and Nc are the number of unchanged points and changed points of the ground-truth change map; and N is the sum ofSensorsNu and2018,N 18c,, x respectively. FOR PEER REVIEW 7 of 19

2.6. The Proposed Method of Time SeriesSeries ChangeChange DetectionDetection UsingUsing ImagesImages fromfrom DifferentDifferent SensorsSensors The procedureprocedure of of time-series time-series unsupervised unsupervised change chan detectionge detection based based on different on different sensors sensors is shown is inshown Figure in1 .Figure The process 1. The of process change detectionof change includes, detection (1) includes, Data preprocessing, (1) Data preprocessing, including radiometric including correction,radiometric geometric correction, correction, geometric co-registrationcorrection, co-registration and ﬁltering. and (2) filtering. Calculating (2) Calculating the overall the difference overall imagedifference of a image time-series of a time-series image by omnibusimage by testomnibus statistic test and statistic acquiring and acquiring the difference the difference images between images any two images in different times by Rj test statistic. (3) Segmenting the obtained difference images between any two images in different times by R j test statistic. (3) Segmenting the obtained by the GSRM algorithm. (4) Modeling the segmented difference images by GGMM model, obtaining difference images by the GSRM algorithm. (4) Modeling the segmented difference images by the statistical distribution of change and no-change classes. (5) Making decision analysis according to GGMM model, obtaining the statistical distribution of change and no-change classes. (5) Making Equation (21) and calculating the change: decision analysis according to Equation (21) and calculating the change: ( 255,255,pp( (wwpxwu )p (x |wu ) < pwpxwp ((w )c) (p( |x|w )c) CD(i, j) =  uucc (21) CD(i, j)  0, otherwise (21) 0, otherwise where,where, ‘0’‘0’ representsrepresents no-changeno-change classclass andand ‘255’‘255’ representsrepresents changechange classes.classes.

Figure 1. The procedure of the proposed method.

3. Experiments and Results

3.1. Study Area and Background The citycity ofof WuhanWuhan (as(as shownshown inin FigureFigure2 )2) lies lies at at East East longitude longitude 113 113°41◦410–115′–115°05◦050′,, NorthNorth latitudelatitude 2929°58◦580′–31°22–31◦22′0,, and and is is the the only only megalopolis megalopolis in the west of China. Wuhan is known as ‘River City’City’ asas itit isis wherewhere thethe thirdthird largestlargest riverriver (Yangtze(Yangtze River)River) inin thethe worldworld andand thethe largestlargest tributarytributary of the Yangtze River (Han(Han River)River) converge. converge. There There are are also also many many lakes lakes in Wuhan,in Wuhan, such such as Eastas East Lake, Lake, LiangZi LiangZi Lake Lake and soand on. so Dramaticon. Dramatic changes changes have have taken taken place place in the in city the ofcity Wuhan of Wuhan during during the ‘Twelfth the ‘Twelfth Five-Year Five-Year Plan’ periodPlan’ period (from (from 2011 to 2011 2015). to 2015). Furthermore, Furthermore, the continuous the continuous heavy heavy rain occurred rain occurred in July in 2016 July and 2016 some and areassome showedareas showed dramatic dramatic change, especiallychange, especially LiangZi Lake. LiangZi In order Lake. to detectIn order the dramaticto detect changes the dramatic of city andchanges ﬂooded of city regions, and flooded time-series regions, PolSAR time-series images were PolSAR acquired images from were the acquired Radarsat-2 from and the GF-3 Radarsat-2 sensors. Inand this GF-3 study sensors., our aim In wasthis tostudy, detect our the aim dramatic was to changes detect the of citydramatic and ﬂooded changes region of city (LiangZi and flooded Lake) andregion monitor (LiangZi the Lake) changes and associated monitor the with changes the tunnel associ constructionated with the on tunnel East Lake. construction on East Lake.

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FigureFigure 2.2. LocationLocation ofof thethe studystudy areas.areas. Figure 2. Location of the study areas. InIn orderorder toto detectdetect thethe dramaticdramatic changeschanges ofof thethe city,city, the the C-band C-band (single (single look look complex) complex) time-seriestime-series In order to detect the dramatic changes of the city, the C-band (single look complex) time-series fullfull PolSARPolSAR images images of Wuhanof Wuhan were were acquired acquired from Radarsat-2 from Radarsat-2 and Gaofen-3 and Gaofen-3 sensors and sensors the parameters and the full PolSAR images of Wuhan were acquired from Radarsat-2 and Gaofen-3 sensors and the ofparameters the PolSAR of images the PolSAR from theimages different from sensors the differe are shownnt sensors in Table are2 .shown The sizes in ofTable time-series 2. The PolSARsizes of parameters of the PolSAR images from the different sensors are shown in Table 2. The sizes of time-series PolSAR× images are 2400 × 4200 pixels and the PauliRGB images are shown in Figure 3. imagestime-series are 2400 PolSAR4200 images pixels are and 2400 the × PauliRGB4200 pixels imagesand the PauliRGB are shown images in Figure are shown3. in Figure 3.

(a) (b)(c) (a) (b)(c)

(d) (e)

FigureFigure 3. 3.RADARSAT-2 RADARSAT-2 PolSAR PolSAR images images acquired acquired on (a)on 7 December (a) 7 December 2011; (b) 25 2011; June 2015; (b) 25 and June (c) 6 2015; July 2016;(d and) GF-3 PolSAR images acquired on (d()e 30) April 2017; (e) 29 May 2017. and (c) 6 July 2016; and GF-3 PolSAR images acquired on (d) 30 April 2017; (e) 29 May 2017. Figure 3. RADARSAT-2 PolSAR images acquired on (a) 7 December 2011; (b) 25 June 2015; and (c) 6 July 2016; and GF-3 PolSAR images acquired on (d) 30 April 2017; (e) 29 May 2017.

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Table 2. The parameters of the Radarsat-2 and Gaofen-3 images. Table 2. The parameters of the Radarsat-2 and Gaofen-3 images. Acquisition Date Sensors Mode Processing Level Polarization ProductId 7Acquisition December 2011 Date Radarsat-2 Sensors FQ21 Mode Single Processing Look Complex Level HH + Polarization HV + VH + VV RD2011000503-0 ProductId 25 June 2015 Radarsat-2 FQ21 Single Look Complex HH + HV + VH + VV PDS_04516040 7 December 2011 Radarsat-2 FQ21 Single Look Complex HH + HV + VH + VV RD2011000503-0 6 July 2016 Radarsat-2 FQ21 Single Look Complex HH + HV + VH + VV PDS_05215280 25 June 2015 Radarsat-2 FQ21 Single Look Complex HH + HV + VH + VV PDS_04516040 30 6April July 20162017 Gaofen-3Radarsat-2 QPSIFQ21 SingleSingle Look Look Complex Complex HH HH + + HV HV + + VH VH + + VV VV PDS_05215280 2335427 2930 May April 2017 2017 Gaofen-3 Gaofen-3 QPSI QPSI SingleSingle Look Look Complex Complex HH HH + + HV HV + + VH VH + + VV VV 23906862335427 29 May 2017 Gaofen-3 QPSI Single Look Complex HH + HV + VH + VV 2390686 The preprocessing of PolSAR images is crucial to change detection when using time-series PolSAR images from different sensors. The pixel values are related to the backscatter from different The preprocessing of PolSAR images is crucial to change detection when using time-series PolSAR PolSAR data, so the radiometric calibration of data is necessary. In particular, QualifyValue images from different sensors. The pixel values are related to the backscatter from different PolSAR calibration of GF3 data is required using relevant parameters, which can be found in header file of data, so the radiometric calibration of data is necessary. In particular, QualifyValue calibration of GF3 GF3 data. Due to the different projection modes and spatial resolutions of different sensors, it is data is required using relevant parameters, which can be found in header file of GF3 data. Due to necessary to perform geometric correction and co-registration on the time-series PolSAR images the different projection modes and spatial resolutions of different sensors, it is necessary to perform using PolSARPro_v4.2.0 software and NEST software and the Root-Mean-Square Errors (RMSE) of geometric correction and co-registration on the time-series PolSAR images using PolSARPro_v4.2.0 co-registration were less than one pixel in this study. By comparing some filter methods of software and NEST software and the Root-Mean-Square Errors (RMSE) of co-registration were less decreasing the influence of speckle noise, we found that the Lee Sigma filter has the better balance in than one pixel in this study. By comparing some filter methods of decreasing the influence of speckle the accuracy of results and less time-cost. Moreover, we also found that Lee Sigma filter could better noise, we found that the Lee Sigma filter has the better balance in the accuracy of results and less retain details and preserve the shape of small land parcels when compare with other filters[33]. time-cost. Moreover, we also found that Lee Sigma filter could better retain details and preserve the Therefore, a 7 × 7 Lee Sigma filter was chosen to decrease the influence of noise for the time-series shape of small land parcels when compare with other filters [33]. Therefore, a 7 × 7 Lee Sigma filter PolSAR images. was chosen to decrease the influence of noise for the time-series PolSAR images.

3.2. Results In order to assess the effectiveness of the proposed change detection algorithm, algorithm, OT_GSRM_GGMM experiments experiments were were conducted conducted with with data data from from LiangZi LiangZi Lake Lake and and East East Lake. Lake. As Asmentioned mentioned above, above omnibus, omnibus test test statistic statistic algorithm algorithm is isdesigned designed to to generate generate the the difference difference image image over over R the entire entire time time period, period, and and Rjj test test statistic statistic is is used used to to generategenerate thethe differencedifference imagesimages in any different time intervals.

3.2.1. Omnibus Test Statistic of Time SeriesSeries ChangeChange DetectionDetection overover LiangZiLiangZi LakeLake The datasets used in this research were time-series PolSAR images from Radarsat-2 acquired on 7 December 2011, 25 June 2015 and 6 July 2016, respectively.respectively. The Pauli-RGB images are shown in Figure4 4a–c.a–c. DueDue to to the the restriction restriction of of natural natural conditions conditions and and observation observation data, data, we we can can only only conﬁrm confirm the thechange change caused caused by ﬂooding by flooding on 6 Julyon 6 2016, July around2016, around LiangZi LiangZi Lake. Meanwhile, Lake. Meanwhile, other places other and places changes and changes(such as (such the extension as the extension of the urban) of the inurban) different in diff timeerent were time obtained were obtained by visual by visual interpretation interpretation of the ofoptical the optical imagery imagery of GF-2 of images GF-2 correspondingimages correspondi to theng time to ofthe PolSAR time of images. PolSAR The images. ground The references ground referencesaround LiangZi around Lake LiangZi are shown Lake are in Figure shown4d–g. in Figures 4d–g.

(a) (b)(c)(d)

Figure 4. Cont.

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(e) (f)(g) (e) (f)(g) Figure 4. The Pauli-RGB images of LiangZi Lake after preprocessing on (a) 7 December 2011; (b) 25 Figure 4. TheThe Pauli-RGB Pauli-RGB images images of LiangZi of LiangZi Lake Lake after after preprocessing preprocessing on (a on) 7 December (a) 7 December 2011; (b 2011;) 25 b June 2015;c and (c) 6 July 2016;d (d) Ground reference (2011–2015); (ee) Ground reference (2015–2016); (f) (June) 25 2015; June and 2015 (;c) and6 July ( 2016;) 6 July (d) Ground 2016; ( reference) Ground (2011–2015); reference ( (2011–2015);e) Ground reference ( ) Ground (2015–2016); reference (f) (2015–2016); (fGround) Ground reference reference (2011–2016); (2011–2016); (g ()g Ground) Ground reference reference (2011–2015–2016). (2011–2015–2016). Ground reference (2011–2016); (g) Ground reference (2011–2015–2016). The overall difference image for LiangZi Lake was obtained by omnibus test statistic algorithm The overall difference image for LiangZi Lake waswas obtainedobtained byby omnibusomnibus testtest statisticstatistic algorithmalgorithm using time series (2011–2015–2016) PolSAR images and it is shown in Figure 5a. Affected by speckle using time series (2011–2015–2016) PolSAR images and it is shown in FigureFigure5 5a.a. AffectedAffected byby specklespeckle noise, the difference image obtained by the traditional pixel-based change detection method still noise, the difference image obtainedobtained by thethe traditionaltraditional pixel-basedpixel-based changechange detectiondetection method stillstill contains finely divided spots in Figure 5a. For this reason, the GSRM algorithm was used for contains ﬁnelyfinely divided spots in FigureFigure5 a.5a. ForFor thisthis reason,reason, thethe GSRMGSRM algorithmalgorithm waswas usedused forfor segmentation, and the result after segmentation is shown in Figure 5b. The segmentation parameters segmentation, and the result after segmentation is shown in FigureFigure5 5b.b. TheThe segmentationsegmentation parametersparameters are the scale parameter and gradient threshold, which were set to 32 and 0.5, respectively. are the scale parameter and and gradient gradient threshold, threshold, which which were were set set to to 32 32 and and 0.5, 0.5, respectively. respectively.

(a) (b) (a) (b) Figure 5. (a) The difference image based on omnibus test statistic; (b) The difference image after GSRM. Figure 5. ((aa)) The The difference difference image based on omnibus test statistic; ( b) The difference image after GSRM. From Figure 5b, it can be seen that the speckle noise can be suppressed using the GSRM From Figure 5b, it can be seen that the speckle noise can be suppressed using the GSRM From Figurealgorithm.5b, it Different can be seenexperiments that the ware speckle undertake noise canto verify be suppressed the effectiveness using of the the GSRM proposed method: algorithm. Different experiments ware undertake to verify the effectiveness of the proposed method: algorithm. Different(1) comparison experiments with warethe method undertake of pixel-based to verify the using effectiveness omnibus of test the statistic proposed and method: GGMM algorithm (1) comparison with the method of pixel-based using omnibus test statistic and GGMM algorithm (1) comparison(namedwith theOT_GGMM_pix) method of pixel-based and it is using shown omnibus in Figu testre statistic6a; (2) andcomparison GGMM algorithmwith the method of (named OT_GGMM_pix) and it is shown in Figure 6a; (2) comparison with the method of (named OT_GGMM_pix)object-based andusing it isomnibus shown intest Figure statistic6a; (2) and comparison GGMM algorithm with the method (named of OT_GGMM_obj) object-based and it is object-based using omnibus test statistic and GGMM algorithm (named OT_GGMM_obj) and it is using omnibusshown test in statistic Figure and6b; GGMM(3) compar algorithmison with (named the traditional OT_GGMM_obj) method andusing it TDES is shown algorithm in (named shown in Figure 6b; (3) comparison with the traditional method using TDES algorithm (named Figure6b; (3)OT_GSRM_TDES) comparison with the and traditional it is shown method in Figure using 6c; TDES (4) comparison algorithm (namedwith the OT_GSRM_TDES) traditional method using K&I OT_GSRM_TDES) and it is shown in Figure 6c; (4) comparison with the traditional method using K&I and it is shownalgorithm in Figure (named6c; (4) comparisonOT_GSRM_KI) with and the it traditional is shown methodin Figure using 6d; K&I(5) comparison algorithm (named with the traditional algorithm (named OT_GSRM_KI) and it is shown in Figure 6d; (5) comparison with the traditional OT_GSRM_KI)method and it using is shown k-means in Figure algorithm6d; (5) comparison (named OT_GSRM_Kmeans with the traditional) and method it is using shownk-means in Figure 6e. The method using k-means algorithm (named OT_GSRM_Kmeans) and it is shown in Figure 6e. The algorithm (namedwhite OT_GSRM_Kmeans)color represents change and it information is shown in Figureand bl6e.ack The color white represents color represents no-change change information in white color represents change information and black color represents no-change information in information andchange black detection color represents map. no-change information in change detection map. change detection map.

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(a) (b)(c)

(d) (e)(f)

Figure 6. Change detection results of different algorithms over LiangZi Lake from 2011, 2015, and Figure 6. Change detection results of different algorithms over LiangZi Lake from 2011, 2015, 2016. (a) Change detection result using OT_GGMM_pix (k = 22). (b) Change detection result using and 2016.(a) Change detection result using OT_GGMM_pix (k = 22). (b) Change detection result using OT_GGMM_obj (k = 27). (c) Change detection results using OT_GSRM_TDES. (d) Change detection OT_GGMM_obj (k = 27). (c) Change detection results using OT_GSRM_TDES. (d) Change detection results using OT_GSRM_KI. (e) Change detection results using OT_GSRM_Kmeans. (f) Change results using OT_GSRM_KI. (e) Change detection results using OT_GSRM_Kmeans. (f) Change detection results using the OT_GSRM_GGMM (k = 26). where, k is the optimal number of GMM. detection results using the OT_GSRM_GGMM (k = 26). where, k is the optimal number of GMM. Comparing the results of different methods in Figure 6, due to the influence of speckle noise, dividedComparing spots can the be resultsfound in of the different result of methods pixel-based in Figure change6, detection due to the algorithm inﬂuence in ofthe speckle Figure 6a, noise, dividedwhich results spots canin a be higher found FA. in theObject-oriented result of pixel-based change detection change detectionalgorithm algorithmcan better insuppress the Figure the 6a, whichspeckle results noise, in but a higherthe methodFA. Object-orientedcannot maintain changethe details detection of the changes algorithm and can a higher better OF suppress can be the specklefound noise,in the butFigure the method6b. The cannotproposed maintain method the not details only of can the changesbetter suppress and a higher the speckleOF can noise, be found inmaintain the Figure the6 b.details The proposedof the changes, method but not also only improve can better the suppress OA and theKappa speckle when noise, compare maintain with the detailstraditional of the method changes, (such but as also K&I, improve TDES, k-means the OA algorithm)and Kappa fromwhen the compare Table 3. with traditional method (such as K&I, TDES, k-means algorithm) from the Table3. Table 3. Performance evaluation over LiangZi Lake from 2011, 2015, and 2016.

Table 3. PerformanceMethod evaluationOA over LiangZiFA Lake fromOF 2011,Kappa 2015, and 2016. OT_GGMM_pix 89.91% 1.43% 8.65% 0.54 OT_GGMM_objMethod 91.03%OA 1.13% FA 7.83% OF Kappa0.59 OT_GSRM_KIOT_GGMM_pix 92.35%89.91% 1.73%1.43% 5.91% 8.65% 0.70 0.54 OT_GSRM_TDESOT_GGMM_obj 92.29%91.03% 2.22%1.13% 5.70 7.83%% 0.69 0.59 OT_GSRM_KmeansOT_GSRM_KI 90.87%92.35% 0.32%1.73% 8.80% 5.91% 0.590.70 OT_GSRM_GGMMOT_GSRM_TDES 93.85%92.29% 0.27%2.22% 6.87% 5.70% 0.710.69 OT_GSRM_Kmeans 90.87% 0.32% 8.80% 0.59 3.2.2. Rj Test Statistics ofOT_GSRM_GGMM Time Series Change 93.85%Detection0.27% over LiangZi 6.87% Lake 0.71 The above results obtained using the omnibus test statistic are binary and they can reflect the 3.2.2.overallRj Testchange Statistics from of2011 Time to Series2016, Changehowever, Detection they cannot over LiangZiexplain Lakewhen the change occurred. However,The above by combining results obtained Rj test usingstatistic the processing, omnibustest OT_GSRM_GGMM statistic are binary algorithm and they can candetect reﬂect the the overallchange change between from two 2011 different to 2016, images however, in any they time cannot intervals. explain We whentherefore the carried change out occurred. three change However, by combining Rj test statistic processing, OT_GSRM_GGMM algorithm can detect the change between

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Sensors 2018, 18, x FOR PEER REVIEW 12 of 19 two different images in any time intervals. We therefore carried out three change detection experiments betweendetection two experiments time intervals between (2011 two and time 2015, intervals 2011 and(2011 2016, and 20152015, and2011 2016). and 2016, The 2015 change and detection2016). resultsThe change are shown detection in Figures results7– are9 respectively. shown in Figures 7–9 respectively.

(a) (b)(c)

(d) (e)(f)

FigureFigure 7. 7.Change Change detection detection resultsresults ofof thethe different algorithms algorithms over over LiangZi LiangZi Lake Lake between between 2011 2011 and and 2015.2015. (a ()a Change) Change detection detection resultresult usingusing OT_GGMM_pixOT_GGMM_pix (k (k == 16). 16). (b ()b )Change Change detection detection result result using using OT_GGMM_obj (k = 14). (c) Change detection results using OT_GSRM_TDES. (d) Change detection OT_GGMM_obj (k = 14). (c) Change detection results using OT_GSRM_TDES. (d) Change detection results using OT_GSRM_KI. (e) Change detection results using OT_GSRM_Kmeans. (f) Change results using OT_GSRM_KI. (e) Change detection results using OT_GSRM_Kmeans. (f) Change detection results using the OT_GSRM_GGMM (k = 21). detection results using the OT_GSRM_GGMM (k = 21). The results of change detection using different algorithms between 2011 and 2015 over LiangZi LakeThe are results represented of change in Figure detection 7, where using it can different be seen algorithms that significant between changes 2011 are and detected. 2015 over The LiangZi OA, LakeFA, areOF representedand Kappa of in different Figure7 ,algorithms where it can are be listed seen in that Table signiﬁcant 4. The quantitative changes are comparison detected. Theof theOA , FAsix, OF detectionand Kappa schemesof different indicates algorithms that the areproposed listed inapproach Table4. Theshows quantitative a better performance comparison than of the the six detectionother approaches schemes indicatesas in the first that experiment. the proposed approach shows a better performance than the other approaches as in the ﬁrst experiment. Table 4. Performance evaluation over LiangZi Lake between 2011 and 2015.

Table 4. PerformanceMethod evaluationOA over LiangZiFA Lake betweenOF Kappa 2011 and 2015. OT_GGMM_pix 94.64% 2.43% 2.93% 0.66 OT_GGMM_objMethod 95.55%OA 2.04% FA 2.41% OF Kappa0.69 OT_GGMM_pixOT_GSRM_KI 95.14%94.64% 2.23%2.43% 2.93%2.63% 0.680.66 OT_GSRM_TDESOT_GGMM_obj 95.16%95.55% 2.13%2.04% 2.41%2.71% 0.680.69 OT_GSRM_KmeansOT_GSRM_KI 95.20%95.14% 2.31%2.23% 2.63%2.49% 0.68 OT_GSRM_GGMMOT_GSRM_TDES 96.98%95.16% 1.29%2.13% 2.71%1.73% 0.780.68 OT_GSRM_Kmeans 95.20% 2.31% 2.49% 0.68 We also comparedOT_GSRM_GGMM and analyzed change 96.98% detection1.29% results 1.73% between 0.78 2011 and 2016, 2015 and 2016. Analogously, the results shown in Figures 8 and 9 together with OA, FA, OF and Kappa (listed in TablesWe also 5 comparedand 6). These and results analyzed further change confirm detection that the results proposed between method 2011 obtains and 2016, better 2015 a change and 2016. Analogously,detection results. the results shown in Figures8 and9 together with OA, FA, OF and Kappa (listed in

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Tables5 and6). These results further conﬁrm that the proposed method obtains better a change detectionSensors 2018 results., 18, x FOR PEER REVIEW 13 of 19

(a) (b)(c)

(d) (e)(f)

FigureFigure 8. 8.Change Change detection detection resultsresults ofof thethe differentdifferent algorithms over over LiangZi LiangZi Lake Lake between between 2011 2011 and and 2016. (a) Change detection result using OT_GGMM_pix (k = 18). (b) Change detection result using 2016. (a) Change detection result using OT_GGMM_pix (k = 18). (b) Change detection result using OT_GGMM_obj (k = 14). (c) Change detection results using OT_GSRM_TDES. (d) Change detection OT_GGMM_obj (k = 14). (c) Change detection results using OT_GSRM_TDES. (d) Change detection results using OT_GSRM_KI. (e) Change detection results using OT_GSRM_Kmeans. (f) Change results using OT_GSRM_KI. (e) Change detection results using OT_GSRM_Kmeans. (f) Change detection results using the OT_GSRM_GGMM (k = 22). detection results using the OT_GSRM_GGMM (k = 22).

Table 5. Performance evaluation over LiangZi Lake between 2011 and 2016. Table 5. Performance evaluation over LiangZi Lake between 2011 and 2016. Method OA FA OF Kappa OT_GGMM_pixMethod 92.46%OA 1.23% FA OF6.29% Kappa0.58 OT_GGMM_obj 94.03% 0.57% 5.38% 0.65 OT_GSRM_KIOT_GGMM_pix 93.16%92.46% 1.23% 0.82% 6.29% 6.01% 0.58 0.61 OT_GGMM_obj 94.03% 0.57% 5.38% 0.65 OT_GSRM_TDES 93.29% 0.75% 5.95% 0.62 OT_GSRM_KI 93.16% 0.82% 6.01% 0.61 OT_GSRM_Kmeans 93.28% 0.75% 5.96% 0.62 OT_GSRM_TDES 93.29% 0.75% 5.95% 0.62 OT_GSRM_GGMM 95.95% 0.60% 3.44% 0.71 OT_GSRM_Kmeans 93.28% 0.75% 5.96% 0.62 OT_GSRM_GGMM 95.95% 0.60% 3.44% 0.71 Table 6. Performance evaluation over LiangZi Lake between 2015 and 2016.

Table 6. PerformanceMethod evaluationOA over LiangZiFA Lake betweenOF 2015Kappa and 2016. OT_GGMM_pix 95.12% 2.07% 2.97% 0.70 OT_GGMM_objMethod 95.41%OA 1.90% FA 2.74% OF Kappa0.73 OT_GSRM_KI 95.30% 3.51% 1.17% 0.74 OT_GGMM_pix 95.12% 2.07% 2.97% 0.70 OT_GSRM_TDES 95.49% 2.12% 2.38% 0.74 OT_GGMM_obj 95.41% 1.90% 2.74% 0.73 OT_GSRM_Kmeans 94.29% 4.58% 1.12% 0.71 OT_GSRM_KI 95.30% 3.51% 1.17% 0.74 OT_GSRM_GGMM 96.22% 1.56% 2.20% 0.76 OT_GSRM_TDES 95.49% 2.12% 2.38% 0.74 OT_GSRM_Kmeans 94.29% 4.58% 1.12% 0.71 OT_GSRM_GGMM 96.22% 1.56% 2.20% 0.76

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(a()a ) (b)()(c) c)

(d) (e)(f) (d) (e)(f) Figure 9. Change detection results of the different algorithms over LiangZi Lake between 2015 and Figure 9. Change detection results of the different algorithms over LiangZi Lake between 2015 and Figure2016. 9.(a )Change Change detection detection resultsresult usingof the OT_GGMM_pix different algorithms (k = 21). over (b) LiangZiChange Lakedetection between result 2015 using and 2016. (a) Change detection result using OT_GGMM_pix (k = 21). (b) Change detection result using 2016.OT_GGMM_obj (a) Change detection (k = 33). ( cresult) Change using detection OT_GGMM_pix results using (k OT_GSRM_TDES.= 21). (b) Change ( ddetection) Change resultdetection using OT_GGMM_obj (k = 33). (c) Change detection results using OT_GSRM_TDES. (d) Change detection OT_GGMM_objresults using OT_GSRM_KI. (k = 33). (c) Change (e) Change detection detection results results using usingOT_GSRM_TDES. OT_GSRM_Kmeans. (d) Change (f) Change detection results using OT_GSRM_KI. (e) Change detection results using OT_GSRM_Kmeans. (f) Change resultsdetection using results OT_GSRM_KI. using the OT_GSRM_GGMM (e) Change detection (k = 25). results using OT_GSRM_Kmeans. (f) Change detection results using the OT_GSRM_GGMM (k = 25). detection results using the OT_GSRM_GGMM (k = 25). Through a comprehensive comparison of omnibus test statistic and Rj test statistic change

ThroughdetectionThrough results, a a comprehensive comprehensive it is clear that comparisoncomparison omnibus test of statistic omnibus result test test (Figure statistic statistic 6f) and reveals and RRj thejtesttest overall statistic statistic change change change R detectiondetectionfrom results,2011 results, to it2016, isit clearis while clear that thatj omnibus test omnibus statistic test test results statistic statistic (Figures result result (Figure10a– (Figurec) 6displayf) reveals6f) revealsthe thechange overallthe overallfor changedifferent change from 2011 to 2016, while R test statistic results (Figure 10a–c) display the change for different locations and fromlocations 2011 toand 2016, areasj while between Rj testtwo statistic different results times. (FiguresArea 1 marked10a–c) displayby a red the rectangular change for box different is a areasconspicuous between two change different area between times. Area year 12011 marked and 2015. by a In red the rectangular same location, box the is asignificant conspicuous change change locations and areas between two different times. Area 1 marked by a red rectangular box is a also took place between 2015 and 2016 (Figure 10c), while there was little change between year 2011 areaconspicuous between year change 2011 area and between 2015. In year the same 2011 location,and 2015. the In signiﬁcantthe same location, change alsothe significant took place change between and 2015. The reason for this is that the white part in area 1 was covered with water in year 2011 2015also and took 2016 place (Figure between 10c), 2015 while and there 2016 was(Figure little 10c), change while between there was year little 2011 change and 2015.between The year reason 2011 for this isand that 2016 the and white but partwas dry in arealand 1in was 2015. covered Moreover, with from water the white in year part 2011 of area and 2 2016 which and identically but was dry andmarked 2015. Theby a reason red rectangular for this is box, that wethe can white also part see inthe area different 1 was changescovered between with water different in year time 2011 land in 2015. Moreover, from the white part of area 2 which identically marked by a red rectangular andintervals. 2016 and Affected but was by drythe floodingland in 2015.in 2016, Moreover, the change from range the between white part 2011 of and area 2016, 2 which 2015 and identically 2016 box,marked weare larger can by also thana red see the rectangular the change different between box, changes 2011we can and between also 2015. see different the different time intervals. changes between Affected different by the ﬂooding time in 2016intervals., the changeAffected range by the between flooding 2011 in 2016, and the 2016, change 2015 range and 2016 between are larger 2011 and than 2016, the change2015 and between 2016 2011are and larger 2015. than the change between 2011 and 2015.

(a)(b)(c)

Figure 10. Change detection results (a) Between 2011 and 2015 using the OT_GSRM_GGMM. (b) Between 2011(a)( and 2016 using the OT_GSRM_GGMM.b)( (c) Between 2015 andc) 2016 using the OT_GSRM_GGMM. FigureFigure 10. 10.Change Change detectiondetection resultsresults (a) Between 2011 2011 and and 2015 2015 using using the the OT_GSRM_GGMM. OT_GSRM_GGMM.

(b)(b Between) Between 2011 2011 and and 2016 2016 using using thethe OT_GSRM_GGMM.OT_GSRM_GGMM. ( (cc)) Between Between 2015 2015 and and 2016 2016 using using the the OT_GSRM_GGMM.OT_GSRM_GGMM.

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3.2.3.3.2.3. Time-SeriesTime-Series ChangeChange DetectionDetection over the East Lake Tunnel TheThe validityvalidity ofof thethe proposedproposed algorithmalgorithm applied to to different sensors sensors was was verified. veriﬁed. From From 2011 2011 to to 2017,2017, constructionconstruction of of a a new new tunnel tunnel and and its ancillaryits ancillary buildings buildings took took place place on East on East Lake Lake in Wuhan, in Wuhan, China. AfterChina. the After new the tunnel new was tunnel constructed, was constructed, the ancillary the buildingsancillary buildings were removed were inremoved 2016. The in land-cover2016. The typesland-cover of this types area areof lake,this bridge,area are city, lake, and bridge, forest. PolSARcity, and images forest. acquired PolSAR by images Radarsat-2 acquired and GF-3by sensorsRadarsat-2 display and theGF-3 changes sensors associated display the with changes the construction associated with of the the new construction tunnel on of East the Lakenew andtunnel the RGBon East images(1200 Lake and rows,the RGB 1000 images(1 columns)200 inrows, Pauli 1000 basis columns) are shown in Pauli in Figure basis 11 are. shown in Figure 11.

(a) (b)(c)

(d) (e)

FigureFigure 11. RADARSAT-2RADARSAT-2 PolSAR PolSAR images images acquired acquired on (a on) 7 December (a) 7 December 2011. (b 2011.) 25 June (b )2015. 25 June(c) 6 July 2015. 2016; and GF-3 PolSAR images acquired on (d) 30 April 2017. (e) 29 May 2017. (c) 6 July 2016; and GF-3 PolSAR images acquired on (d) 30 April 2017. (e) 29 May 2017.

The proposed OT_GSRM_GGMM method was used to detect the changes in the construction processThe of proposed East Lake OT_GSRM_GGMM tunnel. Dramatic changes method took was place used around to detect East the Lake changes from in 2011 the to construction 2017 and processthe changes of East of Lakedifferent tunnel. periods Dramatic over East changes Lake took are shown place around in Figure East 12. Lake from 2011 to 2017 and the changes of different periods over East Lake are shown in Figure 12.

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(a)

(b) (c)

(d) (e) (f)

(g) (h)(i)(j)

Figure 12. Time-series change detection over tunnel of East Lake from 2011 to 2017 (a) between Figure 12. Time-series change detection over tunnel of East Lake from 2011 to 2017 (a) between December 2011 and June 2015. (b) Between December 2011 and July 2016. (c) Between June 2015 and December 2011 and June 2015. (b) Between December 2011 and July 2016. (c) Between June 2015 July 2016. (d) Between December 2011 and April 2017. (e) Between June 2015 and April 2017. and July(f) Between 2016. (Julyd) Between 2016 and DecemberApril 2017. 2011(g) Between and April December 2017. 2011 (e) Betweenand May 2017. June ( 2015h) Between and April June 2017. (f) Between2015 andJuly May 2016 2017. and (i) Between April 2017. July 2016 (g) Between and May 2017. December (j) Between 2011 April and May2017and 2017. May (h 2017.) Between June 2015 and May 2017. (i) Between July 2016 and May 2017. (j) Between April 2017and May 2017. To give a visual impression of the results, Figure 12 shows the time-series change detection Toresults give over a visualthe East impression Lake tunnel from of the 2011 results, to 2017. Figure Figure 1212a shows shows the the change time-series detection change result from detection resultsDecember over the 2011 East to LakeJune tunnel2015, where from the 2011 changes to 2017. reflect Figure the 12constructiona shows theof the change new detectiontunnel and result urbanization. Figure 12b shows the change detection result from 2011 to 2016, where the changes from December 2011 to June 2015, where the changes reflect the construction of the new tunnel and again reflect the construction of the new tunnel and urbanization. Figure 12c shows the change urbanization. Figure 12b shows the change detection result from 2011 to 2016, where the changes again detection result from June 2015 to July 2016, where the changes reflect the removal of the ancillary reflectbuildings the construction and urbanization. of the newFigures tunnel 12d,g andshow urbanization. the change detection Figure result 12c showss from December the change 2011 detection to resultApril from 2017 June and 2015 from to JulyDecember 2016, where2011 to theMay changes 2017, wh reflectere the the changes removal reflect of the the ancillary urbanization. buildings and urbanization.Figures 12e,h show Figure the 12 changed,g show detection the change results detectionfrom December results 2011 from to DecemberApril 2017 and 2011 from to April June 2017 andfrom December 2011 to May 2017, where the changes reflect the urbanization. Figure 12e,h show the change detection results from December 2011 to April 2017 and from June 2015 to May 2017, respectively, where the changes reflect the removal of the ancillary buildings and urbanization. Sensors 2018, 18, 559 17 of 19

Figure 12f,i show the change detection results from July 2016 to April 2017 and from July 2016 to May 2017, where the changes reﬂect the removal of ancillary buildings. Figure 12j shows the change detection result from April 2017 to May 2017, where few changes can be observed.

4. Discussion Many existing unsupervised change detection methods using PolSAR images are limited by detecting change two images with different times from same sensor, and the results are subject to the inﬂuence of speckle noise. In this paper, focusing on the aforementioned problems of existing unsupervised change detection methods, we have proposed an automatic time-series unsupervised change detection approach (OT_GSRM_GGMM) using PolSAR images from different sensors. On the one hand, compared with the traditional methods of change detection, the proposed method has certain advantages:

(1) In the aspect of generation of the difference images, many different methods, such as the log-ratio operator, the hidden Markov chain model, and Kullback-Leibler divergence, were applied in multi-temporal single-channel SAR change detection and test statistics was applied in full PolSAR images. However, these traditional methods can only generate the difference image between two different times and they cannot generate the time-series difference images. Fortunately, omnibus test statistics can generate the difference image over the entire time series and Rj test statistics can generate the difference images for the different time intervals in this paper. (2) With regard to change detection analysis, some thresholding or clustering algorithms, such as k-means algorithm, fuzzy c-means algorithm, Otsu’s thresholding algorithms, Kapur’s entropy algorithms and K&I thresholding algorithm, can better analyze the difference image based on assumption that the PDF is Gaussian distributed for the changed and unchanged classes, but they are not suitable to analyze difference image of a non-Gaussian distribution. However, GGMM is capable of better ﬁtting the arbitrarily conditional densities of the classes and it can also select the optimal number of components for the GMM in proposed method. (3) In terms of image denoising, the object-oriented change detection algorithm can better suppress the inﬂuence of speckle noise, but some of the traditional segmentation algorithms cannot maintain the consistency between segmentation of time-series PolSAR images, the proposed method can avoid the inconsistency of segmentation by segmenting directly time-series difference images.

On the other hand, there still has some limitations in our proposed method:

(1) The design of our method is relatively complicated structure when compare with traditional methods of change detection. (2) The experiment areas only chosen in urban and additional scenes, such as crop growing with different seasons, the change of suspended sediment concentration from different periods, were not considered yet.

5. Conclusions To overcome the limitations of the existing unsupervised change detection methods, an unsupervised change detection method using time-series of PolSAR images was proposed in this paper, which integrates advantages of the omnibus test statistic, GSRM, and GGMM techniques in this paper. The omnibus test statistic algorithm was designed to detect the changes over the entire time period, and Rj test statistics were used to detect changes in different time intervals. To suppress the inﬂuence of speckle noise, the GSRM algorithm was applied to segment difference images and GGMM was used to obtain time-series change detection maps. Using the proposed method, we were able to detect accurately the changes associated with the construction of a tunnel on East Lake from 2011 to 2017, dramatic changes in the city of Wuhan during ‘Twelfth Five-Year Plan‘ period, and also the Sensors 2018, 18, 559 18 of 19 changes of water bodies caused by the heavy rainfall in July 2016. The experimental results indicates that OT_GSRM_GGMM can not only detect small, gradual changes, but improve overall accuracy of the change detection result when compared with traditional unsupervised change detection methods. However, some future further improvements will still be necessary. For example, the computing efﬁciency can be improved by using the GPU technique and the time-series PolSAR images could be segmented using new segmentation techniques, such as watershed transform [34] and the level-set method [35]. Meanwhile, more experiments with additional scenes should be conducted and it also be of interest to try to detect seasonality in the changes.

Acknowledgments: The authors would like to thank the National Natural Science Foundation of China (No. 91438203, No. 61371199, No. 41501382, No. 41601355), the Hubei Provincial Natural Science Foundation (No. 2015CFB328, No. 2016CFB246), and the Technology of Target Recognition Based on GF-3 Program (No. 03-Y20A10-9001-15/16). Author Contributions: Wensong Liu defined the research problem, proposed methods, undertook most of the programming, and wrote the paper. Jie Yang gave some key advice. Jinqi Zhao gave useful advice and contributed to the paper writing, Hongtao Shi processed the datasets by PolSARpro and NEST software, contributed to the paper writing. Le Yang provided the ground truth. Conflicts of Interest: The authors declare no conflict of interest.

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