SAR Image Classification via Hierarchical Sparse Representation and Multisize Patch Features

Biao Hou, Bo Ren, Guilin Ju, Huiyan Li, Licheng Jiao, Jin Zhao

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.