scholarly journals Unsupervised Classification of Polarimetric SAR Image Based on Geodesic Distance and Non-Gaussian Distribution Feature

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1317
Author(s):  
Junrong Qu ◽  
Xiaolan Qiu ◽  
Chibiao Ding ◽  
Bin Lei
Keyword(s):  
Geodesic Distance ◽  
Image Interpretation ◽  
Unsupervised Classification ◽  
Wishart Distribution ◽  
Surface Scattering ◽  
Polarimetric Synthetic Aperture Radar ◽  
Initial Segmentation ◽  
L Band ◽  
Non Gaussian

Polarimetric synthetic aperture radar (PolSAR) image classification plays a significant role in PolSAR image interpretation. This letter presents a novel unsupervised classification method for PolSAR images based on the geodesic distance and K-Wishart distribution. The geodesic distance is obtained between the Kennaugh matrices of the observed target and canonical targets, and it is further utilized to define scattering similarity. According to the maximum scattering similarity, initial segmentation is produced, and the image is divided into three main categories: surface scattering, double-bounce scattering, and random volume scattering. Then, using the shape parameter α of K-distribution, each scattering category is further divided into three sub-categories with different degrees of heterogeneity. Finally, the K-Wishart maximum likelihood classifier is applied iteratively to update the results and improve the classification accuracy. Experiments are carried out on three real PolSAR images, including L-band AIRSAR, L-band ESAR, and C-band GaoFen-3 datasets, containing different resolutions and various terrain types. Compared with four other classic and recently developed methods, the final classification results demonstrate the effectiveness and superiority of the proposed method.

scholarly journals Novel Techniques for Built-Up Area Extraction from Polarimetric SAR Images

2020 ◽  
Author(s):  
D Ratha ◽  
P Gamba ◽  
A Bhattacharya ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Unit Sphere ◽  
Geodesic Distance ◽  
Synthetic Aperture ◽  
Extraction Techniques ◽  
Remote Sensing Images ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Scenes ◽  
L Band

© 2004-2012 IEEE. Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of Kennaugh matrices. The first method is based on the three dominant scattering types in the scene and compares them with scattering models; if any of them matches with BU type elementary scattering models, then the pixel is said to belong to a BU area. The second method is based on a novel PolSAR BU index (RBUI) composed by considering scattering mechanisms from BU structures. The two proposed techniques are validated on two different urban scenes, one acquired at C-band by RADARSAT-2 and other at L-band by ALOS-2 SAR sensors.

scholarly journals Novel Techniques for Built-Up Area Extraction from Polarimetric SAR Images

2020 ◽  
Author(s):  
D Ratha ◽  
P Gamba ◽  
A Bhattacharya ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Unit Sphere ◽  
Geodesic Distance ◽  
Synthetic Aperture ◽  
Extraction Techniques ◽  
Remote Sensing Images ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Scenes ◽  
L Band

© 2004-2012 IEEE. Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of Kennaugh matrices. The first method is based on the three dominant scattering types in the scene and compares them with scattering models; if any of them matches with BU type elementary scattering models, then the pixel is said to belong to a BU area. The second method is based on a novel PolSAR BU index (RBUI) composed by considering scattering mechanisms from BU structures. The two proposed techniques are validated on two different urban scenes, one acquired at C-band by RADARSAT-2 and other at L-band by ALOS-2 SAR sensors.

scholarly journals A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameter-Based Unsupervised Classification Scheme Using a Geodesic Distance

2020 ◽  
Author(s):  
D Ratha ◽  
E Pottier ◽  
A Bhattacharya ◽  
Alejandro Frery
Keyword(s):  
San Francisco ◽  
Classification Scheme ◽  
Geodesic Distance ◽  
Unsupervised Classification ◽  
Total Power ◽  
Polarimetric Synthetic Aperture Radar ◽  
Invariant Parameter ◽  
Decomposition Schemes ◽  
Type Parameter ◽  
Power Component

© 1980-2012 IEEE. We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a geodesic distance between pairs of 4× 4 real Kennaugh matrices. In standard model-based decomposition schemes, the 3× 3 Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the geodesic distance (GD) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter (αGD), helicity parameter (τ GD), and purity parameter (PGD). A PGD/αGD unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.

scholarly journals Analysis of Stochastic Distances and Wishart Mixture Models Applied on PolSAR Images

2019 ◽  
Vol 11 (24) ◽  
pp. 2994 ◽  
Author(s):  
Naiallen Carolyne Rodrigues Lima Carvalho ◽  
Leonardo Sant’Anna Bins ◽  
Sidnei João Siqueira Sant’Anna
Keyword(s):  
Em Algorithm ◽  
Unsupervised Classification ◽  
Wishart Distribution ◽  
Classification Performance ◽  
Amazon Forest ◽  
Bhattacharyya Distance ◽  
Chi Square ◽  
Em Algorithms ◽  
Polarimetric Synthetic Aperture Radar ◽  
The Em Algorithm

This paper address unsupervised classification strategies applied to Polarimetric Synthetic Aperture Radar (PolSAR) images. We analyze the performance of complex Wishart distribution, which is a widely used model for multi-look PolSAR images, and the robustness of five stochastic distances (Bhattacharyya, Kullback-Leibler, Rényi, Hellinger and Chi-square) between Wishart distributions. Two unsupervised classification strategies were chosen: the Stochastic Clustering (SC) algorithm, which is based on the K-means algorithm but uses stochastic distance as the similarity metric, and the Expectation-Maximization (EM) algorithm for Wishart Mixture Model. With the aim of assessing the performance of all algorithms presented here, we performed a Monte Carlo simulation over a set of simulated PolSAR images. A second experiment was conducted using the study area of Tapajós National Forest and the surrounding area, in Brazilian Amazon Forest. The PolSAR images were obtained by the satellite PALSAR. The results, in both experiments, suggest that the EM algorithm and the SC with Hellinger and the SC with Bhattacharyya distance provide a better classification performance. We also analyze the initialization problem for SC and EM algorithms, and we demonstrate how the initial centroid choice influences the final classification result.

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