SAR Image Content Retrieval Based on Fuzzy Similarity and Relevance Feedback

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Abstract
This paper presents a new content-based synthetic aperture radar (SAR) image retrieval method to search out SAR image patches, which consists of two essential parts: an initial retrieval and later refined results. To obtain the proper initial retrievals, we develop a similarity measure named region-based fuzzy matching (RFM) to evaluate the similarities between SAR image patches. First, to reduce the negative influence of speckle noise, we segment the SAR image patches into brightness-texture regions at the superpixel level rather than the pixel level. Second, a multiscale edge detector is utilized to resolve the multiscale property of the SAR image patches, and then the edge regions of the SAR image patches are defined by those edge features. Third, to overcome the segmented uncertainty and the blurry boundaries, the obtained regions are described by fuzzy features. Finally, the RFM similarity between two SAR image patches is converted into the linear summation of the resemblance between different fuzzy feature sets. After we obtain the initial retrieval results, the multiple relevance feedback (MRF) scheme is proposed to refine the original ranked list. In this scheme, different relevance feedback approaches are carried out respectively, and then their results are fused to improve the initial retrieval. In addition, a new kernel function based on the RFM measure is developed for MRF. The encouraging experimental results counted on a manually constructed ground truth SAR image patch dataset demonstrate that our retrieval method is effective for SAR images compared with some existing approaches proposed in the remote sensing community.
Funding Information
  • National Basic Research Program (973 Program) of China (2013CB329402)
  • National Natural Science Foundation of China (61573267, 61473215, 61571342, 61572383, 61501353, 61502369, 61271302, 61272282, 61202176)
  • Foreign Scholars in University Research and Teaching Programs (B07048)
  • National Natural Science Foundation of China (91438201, 91438103)
  • Cheung Kong Scholars and Innovative Research Team in University (IRT_15R53)

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