Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images

Abstract

A multi-layer classification approach based on multi-scales and multi-features (ML–MFM) for synthetic aperture radar (SAR) images is proposed in this paper. Firstly, the SAR image is partitioned into superpixels, which are local, coherent regions that preserve most of the characteristics necessary for extracting image information. Following this, a new sparse representation-based classification is used to express sparse multiple features of the superpixels. Moreover, a multi-scale fusion strategy is introduced into ML–MFM to construct the dictionary, which allows complementation between sample information. Finally, the multi-layer operation is used to refine the classification results of superpixels by adding a threshold decision condition to sparse representation classification (SRC) in an iterative way. Compared with traditional SRC and other existing methods, the experimental results of both synthetic and real SAR images have shown that the proposed method not only shows good performance in quantitative evaluation, but can also obtain satisfactory and cogent visualization of classification results.