期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 18, 期 9, 页码 1600-1604出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3005973
关键词
Edge detection; one iteration merging; superpixel generation; synthetic aperture radar (SAR) image
类别
资金
- Science and Technology on Near-Surface Detection Laboratory Foundation of China [6142414180810]
- National Nature Science Foundation of China [41801236]
The proposed edge-aware superpixel generation method, ESOM, overcomes the limitations of existing methods by only requiring one iteration merging without determining the number of iterations. Experimental results demonstrate the superior performance of the proposed method compared to state-of-the-art methods on real SAR images.
Most of the existing superpixel generation methods are based on local iterative clustering. However, such methods have the following shortcomings: 1) these methods require several iterations and the number of iterations is difficult to determine and 2) the generated superpixel lacks explicit connectivity without a postprocessing step. Aiming to overcome the limitations, we propose an edge-aware superpixel generation with one iteration merging (ESOM) for synthetic aperture radar (SAR) imagery. In specific, we introduce a ratio-based edge detector with a Gaussian-shaped window to extract the edge information and an edge-aware dissimilarity is defined. Then, a new merging method termed as one iteration merging is proposed, which leverages the continuity of the adjacent pixels and ensures the connectivity of superpixel. Furthermore, instead of iterative clustering, the one iteration merging is achieved in only one iteration without determining the number of iterations and hence efficient in computation. Experiments on two real SAR images demonstrate that the proposed method yields substantially better performance than some state-of-the-art methods.
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