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Remote Sens. 2015, 7(7), 8469-8488; doi:10.3390/rs70708469

Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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Author to whom correspondence should be addressed.
Academic Editors: Josef Kellndorfer and Prasad S. Thenkabail
Received: 1 April 2015 / Revised: 12 June 2015 / Accepted: 30 June 2015 / Published: 2 July 2015
View Full-Text   |   Download PDF [1435 KB, uploaded 2 July 2015]   |  

Abstract

Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC). Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR) sensor of the Technical University of Denmark (DTU). Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers. View Full-Text
Keywords: polarimetric SAR; classification; Riemannian manifold; Stein kernel;simultaneous sparse representation; multi-frequency merging polarimetric SAR; classification; Riemannian manifold; Stein kernel;simultaneous sparse representation; multi-frequency merging
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, F.; Gao, W.; Xu, B.; Yang, J. Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation. Remote Sens. 2015, 7, 8469-8488.

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