Application of a New Polarimetric Filter to Radarsat-2 Data of Deception Island (Antarctic Peninsula Region) for Surface Cover Characterization

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APPLICATION OF A NEW POLARIMETRIC FILTER TO RADARSAT-2 DATA OF DECEPTION ISLAND (ANTARCTIC PENINSULA REGION) FOR SURFACE COVER CHARACTERIZATION

a b c a S. Guillaso ,∗ T. Schmid , J. Lopez-Mart´ ´ınez , O. D’Hondt

a Technische Universitat¨ Berlin, Computer Vision and Remote Sensing, Berlin, Germany - [email protected] b CIEMAT, Madrid, Spain - [email protected] c Universidad Autonoma´ de Madrid, Spain - [email protected]

KEY WORDS: SAR, Antarctica, Surface characterization, Geomorphology, Soils, Polarimetry, Speckle Filtering, Image interpretation

ABSTRACT:

In this paper, we describe a new approach to analyse and quantify land surface covers on Deception Island, a volcanic island located in the Northern Antarctic Peninsula region by means of fully polarimetric RADARSAT-2 (C-Band) SAR image. Data have been filtered by a new polarimetric speckle filter (PolSAR-BLF) that is based on the bilateral filter. This filter is locally adapted to the spatial structure of the image by relying on pixel similarities in both the spatial and the radiometric domains. Thereafter different polarimetric features have been extracted and selected before being geocoded. These polarimetric parameters serve as a basis for a supervised classification using the Support Vector Machine (SVM) classifier. Finally, a map of landform is generated based on the result of the SVM results.

1. INTRODUCTION

Ice-free land surfaces of the Northern Antarctica Peninsula region are under the inﬂuence of freeze-thaw cycling effects on different parent materials. This is mainly due to the particular climatic conditions of the so called Maritime Antarctica and because this region is one the the fastest warming areas of the southern hemi- sphere. This contributes to a complex distribution of surface features and soils that are closely related to their abiotic and biotic characteristics. Figure 1: Location of the studied area within (a) Antarctica and (b) the northern Antarctic Peninsula region, including the loca- The use of remotely sensed data is important to detect, study and quantify land surfaces. In order to overcome the problem of harsh tion of the Deception Island. weather conditions as well as the limited accessibility to the areas in this region, the use of synthetic aperture radar (SAR) data is particularly interesting (Schmid, 2012).

The objective of this work is to characterise land surface cover within selected ice-free areas on Deception Island, using fully polarimetric RADARSAT-2 data (C-band), presented in section 2. The strategy of this study is proposed in section 3. where the workflow is presented. Section 4. explains in details the new speckle filter, its results and presents the list of all polarimetric features that will be extracted from the so-called coherency matrix, and the classification scheme that has been applied, using fields data information acquired during several campaigns and a stack of previously selected features, a supervised classification algorithm (Support Vector Machine - SVM) is applied in order to identify the different type of land surface cover present in the scene. Section 5. presents our results and discussion concerning this work.

2. STUDY AREA

The study area is carried out over Deception Island (approx. 62◦ 2 580 S, 60◦ 390 W), a volcanic island of about 72 km located in Figure 2: Fully polarimetric RADARSAT-2 SAR image of De- the South Shetlands archipelago, with an altitude rising to a max- ception Island ( c MacDONALD, DETTWILER AND ASSO- imum of 576 m a.s.l. The location of the site is shown in the Fig- CIATES LTD. 2014 All Rights Reserved). ure 1 (a) and (b). A RADARSAT-2 false-coloured SAR image is

∗Corresponding author presented in Figure 2. The island is a flooded caldera, the volcano From the DEM, a water mask has been extracted and applied to is active and its last eruption occurred in 1970. Deception Island the dataset. Thereafter different polarimetric features have been has been object of many studies in particular regarding its ge- extracted. Selected features having a very high variability have omorphology and geological evolution (Lopez-Mart´ ´ınez, 2002). been selected and generating a separable feature spaces when The variety of surfaces and the active processes in the island make classifying the data. In a next step, data have been geocoded its analysis by means of remote sensing technics of great interest using the nearest neighbour interpolator in order to keep the orig- in terms of identifying characteristics and detecting changes of inal value of each feature. The supervised SVM classifier was ap- surface land covers. plied by using training areas that have been extracted from field campaign and measurements. Finally we obtain a map of surface units. 3. MATERIALS AND METHODS In the following section below, the main steps concerning the use of SAR image are detailled.

4. EXTRACTION OF POLARIMETRIC FEATURES

4.1 Polarimetric SAR data

Polarimetric SAR systems measure the relation between the trans- mitted and received electromagnetic wave in two orthogonal po- larisations in the form of a scattering matrix (Lee, 2009)

s s S = HH HV , (1) sVH sVV

where H and V denote horizontal and vertical polarisation, re- spectively. The reciprocity assumption in the mono-static case leads to sHV = sVH . For the analysing purpose, it is convenient to represent the scattering information in the form of a target vector Figure 3: Full processing chain used in this study. The orange boxes are the ones mainly addressed and described in this paper. 1 T kP = sHH + sVV sHH sVV 2 sHV , (2) √2 − × The Figure 3 represents the processing chain applied in this study. Field data were obtained during several campaigns starting in the 1990s and a most recent one in 2012/2013 aimed at obtaining where P represents the Pauli basis. thematic maps (Lopez-Mart´ ´ınez, 2000; Smellie, 2000) and surface cover data as ground truth information and for validating On distributed targets, which are particularly relevant in the case classification results. A digital elevation model (DEM) and the of our land surface study, the information contained in the scatter- location of the different surface types have been compiled in a ing vector kP is generally considered in a statistical framework. georeferenced database. The final aim of this work is to asso- It is admitted that the target vector k follows a complex circular ciate representative surface covers with satellite images in order d-variate Normal distribution (Goodman, 1963) characterised by to classify them and to analyse their change through the time. its uses of the second order moment Σ = E[kk†] that contains This could used combined with the knowledge about distribution information about power and relative phase between polarimetric of geomorphological features in ice-free surfaces of the region channels. When the scattering vector expressed in the Pauli basis, (e.g. Lopez-Mart´ ´ınez, 2012), to detect surface characteristics in the matrix Σ = T is usually called coherency matrix (although remote not visited areas. it is technically a covariance matrix). Fully polarimetric RADARSAT-2 (C-band, Single Look Com- The Σ has to be estimated from the data over several independent plex) images were acquired from the Canadian Space Agency samples. The covariance matrix is expressed as 2014, over the entire area of Deception Island (see Figure 2). The SAR images have been processed as follows: from the single look complex (SLC) images, a scattering vector has been formed, and L its elements combined to form a coherency matrix, which is the 1 X Σˆ = klk†, (3) basis for many polarimetric treatments. We have applied a new L l l=1 polarimetric speckle filter in order to reduce the speckle effect in the data but also to improve the quality of the resulting coherency matrix. Indeed, this filter is edge preserving but allows a strong and is obtained by an operation called multi-looking that consists smoothing of the homogenous areas. The goal of this new filter in taking the average of contiguous pixels. To improve the estima- is to improve the surface characterisation performed by means of tion accuracy, it is necessary to increase the number of samples, a supervised classification procedure. leading to a loss of spatial structure of the data. Figure 4: Visual comparison and interpretation of applying the PolSAR-BLF filter to RADARSAT-2 data of Deception Island. (a) Filtered SAR image with the location of 2 man-made structures (b) and (c). (b-1,2,3) correspond to Whalers’ Bay, where several old abandonned tanks become clearly visible after applying the filter, (b-3) is the corresponding Google Earth image. (c-1,2,3) correspond to the location of the Spanish Gabriel de Castilla Station. The station is visible in the unfiltered image and the PolSAR-BLF filter is preserving the edge of the station, as the optical image provided by Google Earth shown in (c-3). (d) is the Google Earth image of Deception Island with the location of the selected areas (b) and (c).

4.2 PolSAR-BLF: Polarimetric Bilateral ﬁlter 4.3 Polarimetric Feature Extraction

It has been shown that the PolSAR Bilateral Filter was a useful Polarimetric feature extraction has been extracted from the well known H/A/alpha decomposition of the (3 3) complex coherency tool to estimate the covariance matrix from noisy data (D’Hondt, × 2013). The ﬁlter allows a high amount of speckle ﬁltering in ho- matrix T. From this decomposition, based on an eigendecompo- mogeneous areas and a good restoration of edges and determin- sition of the coherency matrix, we have extracted the following istic scatterers while preserving the polarimetric information. parameters (using PolSARpro, Pottier, 2010):

The estimated covariance at location x0 is computed by perform- Anisotropy: A ing a weighted sum of the sample covariance matrices at each • Anisotropy 12 (over higher eigenvalues): A12 position xi inside a sliding window W : • Scattering angle: α, α1, α2, α3 • X Σ˜ (x0) = wiΣˆ (xi) (4) Scattering angle: β, β!, β2, β3 • xi W ∈ Entropy: H •

The weights wi are expressed by deﬁning a similarity measure Entropy/Anisotropy combination: (1 H)(1 A), (1 • H)A, H(1 A), HA − − − based on a matrix distance d(., .) − Scattering angle: δ, δ1, δ2, δ3 • Double bounce eigenvalue relative difference: DERD fs( xi x0 2)fr[d(R(xi), R(x0)] • wi(xi) = P || − || (5) x W fs( xi x0 2)fr[d(R(xi), R(x0)] Scattering angle: γ, γ1, γ2, γ3 i∈ || − || • Lueneburg Anisotropy: LA • where the functions fs and fr have a Gaussian shape. Pseudo-probability: p1, p2, p3 • The distance between two matrices Σ1 and Σ2 is Polarisation Asymmetry: PA • Polarisation Fraction: PF 1 1 • − 2 − 2 d(R1, R2) = log[R1 R2R1 ] F (6) Radar Vegetation Index: RVI || || • Shannon Entropy: SE where log() is the matrix logarithm and . F is the Frobenius • || || Single bounce eigenvalue relative difference: SERD. norm. •

Figure 4 shows the results of applying our ﬁlter over the entire An illustration of all the extracted polarimetric features obtained island and over two selected areas having man-made structures from RADARSAT-2 data are shown in Figure 5. (Figure 4(b) and (c)). In both cases, we see that the man-made structures become more visible after applying the ﬁlter, but also From these extracted polarimetric features, we select manually the edge of the structures are preserved. The interpretation of the and visually those who have a very high range of variation in image has been done with the help of Google Earth (Figure 4(d)). their values. The results of this selection is presented in Figure 6. ↵ ↵ Pauli RGB A A12 ↵ 1 2 ↵3 1 2

1 H 1 A 1 H A 3 H H 1 A HA 1 2 3 p ´ qp ´ q p ´ q p ´ q

DERD 1 2 3 LA p1 p2 p3 PA

PF PH RV I SE SERD

Figure 5: Polarimetric features of Deception Island extracted from RADARSAT-2 polarimetric data.

1 H 1 A Pauli RGB A A12 p ´ qp ´ q H 1 A 1 2 p ´ q

3 H 1 2 3 RV I SE

Figure 6: Manual selection of the polarimetric features of Deception Island based on their variability.

4.4 Support Vector Machine Classification been extracted using the POLSARPRO software. Among other parameters, entropy (H), alpha (alpha), Anisotropy, the H(1-A) Thereafter, polarimetric features have been geocoded thanks to parameters, and the Shannon-Entropy was extracted. Alpha and the use of an external DEM. A supervised classification with 9 entropy indicate the dominant presence of one scattering mecha- classes was performed using the Support Vector Machine classi- nism per resolution cell, however Anisotropy and Shannon-entropy fication routine in PolSARpro based on the different feature ex- shows strong differences according to their spatial location and tracted above (Pottier, 2010). For each class, between 10 and 20 are essential for implementing the SVM classifier to Deception training areas were selected for the supervised classification. The Island (Figure 7). The classification result shows the identifica- training sites were then selected in PolSARpro to carry out the tion of complex and relatively small scale geo-morphological fea- Support Vector Machine classification without the additional fil- tures such as periglacial landforms that indicate the presence of tering procedure. From the classification results, a map indicating permafrost for the different study areas. the different surface units was generated.

ACKNOWLEDGEMENTS 5. RESULTS AND DISCUSSION This work was supported by the RADARSAT-2 Science and Op- These preliminary results show that SAR backscattering proper- erational Applications Research Program (SOAR) from the Cana- ties using co-polarised or cross-polarised images (HH, HV, VV, dian Space Agency and MacDonald, Dettwiler and Associates VH) can identify different surface covers that are mainly related Ltd. Geospatial Services Inc., Canada, that provided satellite to physical properties. Different polarimetric parameters have data through the SOAR–5169 project granted to the Universidad Characterization of surface features within ice-free areas of the northern Antarctic Peninsula region using RADARSAT-2 data Thomas Schmid1, Stéphane Guillaso2, Jerónimo López-Martínez3, Olivier D'Hondt 2, Tanya O'Neill4, Magaly Koch5, Ana Nieto3, Sandra Mink3,6, Juan José Durán6, Adolfo Maestro3,6,Enrique Serrano7, Megan Balks4

1CIEMAT, Avda. Complutense 40, 28040 Madrid, Spain. [email protected] 2Computer Vision and Remote Sensing Group, Technische Universität Berlin, Office MAR 6-5, Marchstr. 23, Berlin, Germany. stephane@[email protected] 3Dpto. Geología y Geoquímica, Facultad de Ciencias, Universidad Autónoma de Madrid, 28049 Madrid, Spain. [email protected], [email protected], 4Dept. Earth and Ocean Sciences, Waikato University, Hamilton, New Zealand. [email protected], [email protected] 5Center for Remote Sensing, Boston University, Boston, MA, USA. [email protected] 6Instituto Geológico y Minero de España, Ríos Rosas, 23, 28003 Madrid, Spain. [email protected]; [email protected] 7Dpt. Geografía, Universidad de Valladolid, Paseo Prado de la Magdalena s/n, 47011 Valladolid, Spain. [email protected]

Introduction Materials and method

The ice-free areas in the northern Fully polarimetric RADARSAT-2 (C-band, Single Look Complex) images were Antarctic Peninsula region are acquired from the Canadian Space Agency in 2009 and 2014. Field data (Figure 2) dominated by periglacial processes were obtained during several campaigns starting in the 1990s and a most recent one under the influence of maritime in 2012/2013 aimed at obtaining surface cover data and associating representative climate conditions, being this one surface covers with the satellite images. Data processing includes speckle filtering of the fastest warming areas of (D'Hondt et al., 2013), feature and parameter extraction, supervised classification, Antarctica. Depending on the C terrain correction and geocoding (Figure 3). Field data was used to train classifiers parent material, the effects of and validate results obtained from the SAR data. freeze-thaw cycles will influence Field data RADARSAT2 the form and spatial distribution of Quad-Pol data Elevation DEM Speckle filter surface features that will result in a points mosaicked land surface cover. Glacial Feature extraction Periglacial Satellite-borne Synthetic Aperture Topographic and Permafrost thematic maps Supervised classifiers Radar data are ideal for Morpho- structure characterizing and mapping remote Terrain correction Surface and geocoding areas with persistent harsh formations weather conditions and limited Mapping of periglacial access. The objective of this work Landforms 0 2 Figure 2. Field work. is to characterize surface km Figure 3. Processing steps applied. formations within selected ice-free An analysis of the backscattering properties and polarimetric parameters areas on Deception, Livingston and Figure 1. Study area within A) Northern Antarctic associated to the different surface covers were identified. Thereafter polarimetric King George islands, using Peninsular region, b) selected areas, and c) field decompositions and a supervised classification algorithm (Support Vector Machine polarimetric Synthetic Aperture plots of surface covers and full polarimetric RADARSAT-2 satellite data (© MacDONALD, – SVM) were applied. Furthermore, combining the SAR and the DEM data as well Radar (SAR) RADARSAT-2 data DETTWILER AND ASSOCIATES LTD. 2009 – All as field studies (López-Martínez et al., 2012) further helped to differentiate (Figure 1). Rights Reserved). individual periglacial landscape areas.

Pottier, E. 2010. Recent advancesResults in the development of the openand discussion source toolbox for polarimetric and interferometric polarimetric 61°10'W 61°W SAR data processing: the PolSARpro v4.1.5 software. In Inter- 62

national Geoscience and Remote Sensing Symposium (IGARSS). 35‘S ° ° 35‘S Honolulu, Hawaii, USA, IEEE, pp. 2527–2530. 62 396000 399000 Schmid, T., Lopez-Mart´ ´ınez, J., Koch, M., Maestro, A., Ser- rano, E. and Lines,´ C. 2012. Geomorphological mapping in the Antarctic Peninsula region applying single and multipolarization RADARSAT-2 data. Canadian Journal of Remote Sensing, 38(3), pp. 367-382. 3105000 Smellie, J. L. and Lopez-Mart´ ´ınez, J. 2000. Geological Map of Deception Island. BAS Geomap Series, 6-A. E. 1:25 000. Cam- 3105000 bridge, British Antarctic Survey. Gravel and sand deposits (10%) Pavement areas (19%) Stone fields (15%) Patterned ground (18%) Glacial till deposits, rock outcrops (13%) Vegetation covered areas (9%) 62 40‘S ° °

Lakes and flooded coastal regions (3%) 40‘S

62 0 2 km 0 4 km Glacial ice and snow cover areas (13%)

61°10'W 61°W Alluvial deposits of coarse sand (11%) Present-day and Holocene beaches, sediment accumulation 3099000 Pavement areas with regular stone cover (17%) Stone fields, upper playa areas and glacier Stone field with a heterogeneous distribution (14%) Sills, plugs, intrusive igneous bodies and glacial till Fine sediments (9%) 3099000 Igneous sheets, plugs, patterned ground, weathered surfaces Rock outcrops, lava and glacial deposits (6%) Patterned ground, stone fields and debris Gravel and scoria deposits (11%) 0 2 km Lakes and flooded areas (4%) Dispersed rocks and blocks Glacial ice and snow cover areas (29%) 396000 399000 Lakes and pools

FigureFigure 4. Surface 7: Surface cover cover classiﬁcation classification of Deception of Deception Island Figure 5. Surface cover classification of Fildes Peninsula (acquisition Figure 6. Surface cover distribution of Byers Peninsula (RADARSAT-2 acquisition 16 February 2014). Island (acquisition 16 February 2014). 15 March 2009). (acquisition 1 March 2009) Schmid et al., 2012.

Autonoma´ de Madrid, Spain. This is a contribution to the projects PreliminaryCTM2011-26372 results and CTM2014-57119-R show that of the SAR Spain Nationalbackscattering properties using co-polarised or cross-polarised images (HH, HV, VV, VH) can identify different surface covers that R&D National Plan. are mainly related to physical properties. Different polarimetric parameters have been extracted using the POLSARPRO software. Among other parameters, entropy (H), alpha (alpha),REFERENCES Anisotropy, the H(1-A) parameters, and the Shannon-Entropy was extracted. Alpha and entropy indicate the dominant presence of one scattering mechanism per resolution cell, however Anisotropy and Shannon-entropy shows strong differences according to their spatial location and are essential for D’Hondt, O., Guillaso, S. and Hellwich, O., 2013. Iterative bilat- implementingeral ﬁltering of polarimetric the Support SAR data. Accepted Vector for publicationMachine classifier to Deception Island and Fildes Peninsula (Figure 4 and 5). A Wishart supervised classification was applied in the casein IEEEof Byers J. Sel. Topics Peninsula Appl. Earth Observ. (Figure 6) with details described in Schmid et al. (2012). The classification results show the identification of complex and relatively small scale geomorphologicalGoodman, N. R., 1963. Statisticalfeatures analysis such based as on a certainperiglacial landforms that indicate the presence of permafrost for the different study areas. multivariate complex gaussian distribution (an introduction). The Annals of Mathematical Statistics 34, pp. 152177.

Lee, J. and Pottier, E., 2009. Polarimetric Radar Imaging: From Basics to Applications. Optical Science and Engineering, Taylor References & Francis. D’Hondt, O., Guillaso, S., Hellwich, O. 2013. Iterative bilateral filtering of polarimetric SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 1628–1639. ConcludingLopez-Mart´ ´ınez, J.,remarks Serrano, E., Rey, J., Smellie, J. L. 2000. Ge- omorphological Map of Deception Island. BAS Geomap Series, López-Martínez, J., Serrano, E., Schmid, T., Mink, S., Linés, C. 2012. Periglacial processes and landforms in the South Shannon-entropySheet 6-B, 1:25000. has Cambridge, shown British that Antarctic differentiate Survey. complex surface covers such as Shetland Islands (northern Antarctic Peninsula region). Geomorphology, 155–156, 62-79. Schmid, T., López-Martínez, J., Koch, M., Maestro, A., Serrano, E., Linés, C., 2012. Geomorphological mapping in the periglacial areasLopez-Mart´ can´ınez, J.,be Smellie, differentiated J. L., Thomson, J.W.,. Thomson, Antarctic Peninsula region applying single and multipolarization RADARSAT-2 data. Can. J. Remote Sensing, Vol. 38, M.R.A. Eds. 2002. Geology and Geomorphology of Deception An initial Island.distribution BAS Geomap of Series, the Sheets surface 6-A and 6-B Cambridge,cover classes are well identified with the No. 3, pp. 367-382. British Antarctic Survey. 77 p. polarimetric data, and ongoing work is now focused on the validation of these results. Acknowledgements In the future,Lopez-Mart´ polarimetric´ınez, J. ,Serrano, parameters E., Schmid, T., Mink, obtained S., and Lines,´ with RADARSAT-2 data will be studied C. 2012. Periglacial processes and landforms distribution in the This work is supported by Project CTM2011-26372 of the Spanish Antarctic Programme. The authors would like to in more detailSouth to Shetland improve Islands (Northern the classification Antarctic Peninsula region).of periglacial features and surface covers. acknowledge the support of Canadian Space Agency and MDA for providing the satellite data by means of the projects Geomorphology, Vol. 155-156, pp. 62-79. SOAR-1376 and SOAR-5179. Furthermore, the authors thank the Spanish logistic personnel and for the support provided by the Chilean Antarctic Program.