SAR Image Classification via Hierarchical Sparse Representation and Multisize Patch Features
- 6 November 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 13 (1), 33-37
- https://doi.org/10.1109/lgrs.2015.2493242
Abstract
In this letter, a novel hierarchical sparse representation-based classification (HSRC) for synthetic aperture radar (SAR) images is proposed. Features utilized in HSRC are extracted from the multisize patches around each pixel to precisely describe the complex terrains. Two thresholds are introduced in the sparse representation classifier to restrict the range of reconstruction residual, which classifies the reliable classified points, and the rest of the pixels are considered as the uncertain ones in the original SAR image. Then, a new dictionary is constructed by the reliable pixels, and the uncertain pixels will be reclassified in the next classification layer. The hierarchical structure is very reasonable and effective to employ simple features in each layer for describing the various topographic types. Compared with traditional sparse representation-based classification and support vector machines in several fixed-size patches, the proposed method can obtain better performance both in quantitative evaluation and visualization results.Keywords
Funding Information
- National Natural Science Foundation of China (61271302, 61072106)
- National Research Foundation for the Doctoral Program of Higher Education of China (20130203110009)
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