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Remote Sens. 2017, 9(10), 1085; doi:10.3390/rs9101085

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

1,2,* , 1,2,* , 1,2
Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China
Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China
School of Computer Science and Technology, Tianjin University, Tianjin 300072, China
Authors to whom correspondence should be addressed.
Received: 16 August 2017 / Revised: 12 October 2017 / Accepted: 18 October 2017 / Published: 24 October 2017
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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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. View Full-Text
Keywords: sparse representation classification (SRC); multi-layer structure; multi-feature fusion; multi-scale; SAR image sparse representation classification (SRC); multi-layer structure; multi-feature fusion; multi-scale; SAR image

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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Zhai, A.; Wen, X.; Xu, H.; Yuan, L.; Meng, Q. Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images. Remote Sens. 2017, 9, 1085.

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