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Fusion of Polarimetric Features and Structural Gradient Tensors for VHR Polsar Image Classification Minh-Tan Pham

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Fusion of Polarimetric Features and Structural Gradient Tensors for VHR Polsar Image Classification Minh-Tan Pham Fusion of Polarimetric Features and Structural Gradient Tensors for VHR PolSAR Image Classification Minh-Tan Pham To cite this version: Minh-Tan Pham. Fusion of Polarimetric Features and Structural Gradient Tensors for VHR PolSAR Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations andRemote Sensing, IEEE, inPress, 11 (10), pp.3732-3742. 10.1109/JSTARS.2018.2868545. hal-02539261 HAL Id: hal-02539261 https://hal.archives-ouvertes.fr/hal-02539261 Submitted on 9 Apr 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Fusion of Polarimetric Features and Structural Gradient Tensors for VHR PolSAR Image Classification Minh-Tan Pham, Member, IEEE Abstract—This article proposes a fast texture-based supervised [7], the five-component model by Zhang et al. [8], etc. classification framework for fully polarimetric synthetic aperture Based on these decomposition approaches, a great number of radar (PolSAR) images with very high spatial resolution (VHR). recent studies have been proposed to alternatively extract and With the development of recent polarimetric radar remote sensing technologies, the acquired images contain not only rich model the coherency matrix’s information such as the three- polarimetric characteristics but also high spatial content. Thus, component Fisher-based feature weighting approach [9], the the notion of geometrical structures and heterogeneous textures four-component with asymmetric scattering information [10], within VHR PolSAR data becomes more and more significant. the generalized polar decomposition based on Mueller matrix Moreover, when the spatial resolution is increased, we need to [11], the manifold model based on the infinite-dimensional deal with large-size image data. In this work, our motivation is to characterize textures by incorporating (fusing) both polarimetric reproducing kernel Hilbert space [12], etc. However, all of and structural features, and then use them for classification these approaches are limited to the use of polarimetric features purpose. First, polarimetric features from the weighted coherency (derived from the estimated covariance or coherency matrices) matrix and local geometric information based on the Di Zenzo and do not take into account the spatial or structural informa- structural tensors are extracted and fused using the covariance tion from the image content. approach. Then, supervised classification task is performed by using Riemannian distance measure relevant for covariance- The development of very high resolution (VHR) sensors based descriptors. In order to accelerate the computational time, offers PolSAR images including not only the fully polarimetric we propose to perform texture description and classification characteristics but also the significant spatial information. only on characteristic points, not all pixels from the image. Heterogeneous textural and structural content becomes essen- Experiments conducted on the VHR F-SAR data as well as tial and should be taken into account to tackle classification the AIRSAR Flevoland image using the proposed framework provide very promising and competitive results in terms of terrain task. To perform texture-based classification, literature stud- classification and discrimination. ies have proposed to incorporate polarimetric features with several textural measures such as the local binary patterns Index Terms—Polarimetric synthetic aperture radar, textures, gradient tensors, covariance descriptor, riemannian distance, (LBP), the edge histogram descriptors (EHD), the gray level supervised classification cooccurrence matrix (GLCM), the Gabor filter banks (GFB) or the wavelet coefficients [13]–[16]. However, due to the influence of speckle noise, such intensity-based LBP, GLCM, I. INTRODUCTION Gabor or wavelet textural features do not seem to be robust LASSIFICATION of polarimetric synthetic aperture for SAR images. They may considerably depend on the pre- C radar (PolSAR) images has become an active research processing stage of speckle filtering which remains another topic for terrain interpretation and land-use understanding research issue in PolSAR image analysis. Beside that, inspired using remote sensing imagery in the past few years. Thanks to by the successful application of spectral-spatial frameworks the ability to operate under any weather conditions and to cap- using morphological profiles (MPs) and attribute profiles (APs) ture different backscattering characteristics with polarimetric applied to optical images, some studies also proposed to incor- diversity, PolSAR systems offer great advantage compared to porate polarimetric information with multilevel spatial features optical remote sensing imagery [1]. However, PolSAR image obtained by MPs and APs to perform classification task [17], classification task still remains challenging due to the image [18]. Nevertheless, most of these works independently extract perturbation caused by speckle noise and the pixel aggregation polarimetric, spatial and textural features and then concatenate of heterogeneous terrain types with high intensity variation [2]. them to form the combined feature descriptors exploited for Classical methods have been so far using the polarimetric classification step. Thus, their performance still depends on covariance or coherency matrices and their decomposition for every single method to extract each type of features. classification purpose. Some popular methods were proposed In this study, our main motivation is also to incorporate such as the complex Wishart classifier [3], the entropy-based structural features and polarimetric characteristics to tackle approach using the eigen-decomposition of coherency matrix the problem of PolSAR image classification. To do that, we [4], the improved Wishart classifier with H/α initialization propose to first estimate the PolSAR coherency matrix using [5] or various multi-component physical-based decomposi- a weighted multilooking operation based on patch similarity. tion methods including the Freeman-Durden three-component Polarimetric features are derived from these estimated ma- model [6], the four-component model by Yamaguchi et al. trices. Next, the Di Zenzo structural gradient tensors, which were proposed for edge and contour analysis in multi-channel M.T. Pham is with the IRISA laboratory - Universite´ de Bretagne Sud, UMR 6074, Vannes 56000, France. e-mail: [email protected]. images [19], are extracted to capture the image local geometric This work was supported by the Region´ Bretagne grant. properties. Both polarimetric and geometric features are then 2 fused together using a region covariance approach [20]. The induce the over-smoothing phenomenon and hence, cause the obtained covariance-based descriptors are used to perform problem of losing fine details and local structures from the classification task using the support vector machine (SVM) image content. That is the reason why in this work, we classifier based on geometrical affine-invariant Riemannian propose to compute the coherency matrix using a weighted distance metric [21]. In order to accelerate the classification multilooking operator which allows us to take into more process for a fast terrain interpretation, texture description and consideration similar structures from the image during the classification are performed only on characteristic points, not averaging procedure. all pixels of the image. Here, the local extrema (i.e. local The weighted coherency matrix can be estimated for each maximum and local minimum pixels in terms of intensity) will image pixel p as follows: be exploited thanks to their capacity to represent and describe textures within both optical and SAR images [22]–[26]. 1 X H Tp = P wp(q)kqkq ; (1) The remainder of this paper is organized as follows. The wp(q) q2Np q2N next section introduces the proposed framework and describes p each processing stage in details. In Section III, we present in which: the experimental study conducted on two real PolSAR image data. Classification results provided by the proposed algorithm • Np stands for the neighborhood around pixel p, are compared to those yielded by several reference methods. • k is the complex polarimetric scattering vector in Pauli Finally, Section IV concludes the paper and discusses some representation [1]: perspective works. 1 T k = p Shh − Svv 2Shv Shh + Svv ; 2 II. METHODOLOGY An outline of the proposed method can be found in Figure 1. • wp(q) represents the weight function generated for each There are four main processing stages including the estimation neighboring pixel q inside the neighborhood Np: of weighted coherency matrix, the extraction of structure gradient tensors, their fusion to generate covariance descriptors d2(p; q) w (q) = exp − ; (2) on keypoints and the final Riemannian kernel-based supervised p 2 σp classification. We now describe each of them in details. where d(p; q) involves a dissimilarity measure between Input polSAR image two pixels p and q. Weighted Several approaches can be considered to calculate this dis- Multilooking
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