Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation
Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a...
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| Veröffentlicht in: | IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.1972-1985 |
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| container_title | IEEE journal of selected topics in applied earth observations and remote sensing |
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| creator | Shang, Ronghua Lin, Junkai Jiao, Licheng Yang, Xiaohui Li, Yangyang |
| description | Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a superpixel boundary-based edge description algorithm (SpBED) is proposed. First, an edge detection method with three edge detectors is used. Therefore, accurate strong edges of SAR images can be extracted, and false edges that are easy to appear in a single detection method can be effectively eliminated. Then the weak edges of the image are extracted by superpixel generation algorithm. The extracted weak edges can supplement the edge information that is difficult to extract by edge detection. Superpixel boundaries are also used to carry the strong edges, so that the strong and weak edges can be completely represented by superpixel boundaries. Finally, boundary constraint superpixel smoothing is used to reduce the effects of noise, and k-means algorithm is performed on superpixels. Since edge information is carried by superpixels, it effectively guarantees the segmentation accuracy in edge region. Compared with seven state-of-the-art algorithms, segmentation results on simulated images and real images demonstrate the effectiveness of the proposed SpBED. |
| doi_str_mv | 10.1109/JSTARS.2020.2987653 |
| format | Article |
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To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a superpixel boundary-based edge description algorithm (SpBED) is proposed. First, an edge detection method with three edge detectors is used. Therefore, accurate strong edges of SAR images can be extracted, and false edges that are easy to appear in a single detection method can be effectively eliminated. Then the weak edges of the image are extracted by superpixel generation algorithm. The extracted weak edges can supplement the edge information that is difficult to extract by edge detection. Superpixel boundaries are also used to carry the strong edges, so that the strong and weak edges can be completely represented by superpixel boundaries. Finally, boundary constraint superpixel smoothing is used to reduce the effects of noise, and k-means algorithm is performed on superpixels. Since edge information is carried by superpixels, it effectively guarantees the segmentation accuracy in edge region. Compared with seven state-of-the-art algorithms, segmentation results on simulated images and real images demonstrate the effectiveness of the proposed SpBED.</description><identifier>ISSN: 1939-1404</identifier><identifier>EISSN: 2151-1535</identifier><identifier>DOI: 10.1109/JSTARS.2020.2987653</identifier><identifier>CODEN: IJSTHZ</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Algorithms ; Boundaries ; Clustering algorithms ; Computer simulation ; Detection ; Detectors ; Edge detection ; Engineering ; Engineering, Electrical & Electronic ; Geography, Physical ; Image edge detection ; Image processing ; Image segmentation ; Imaging Science & Photographic Technology ; Linear programming ; Methods ; Noise reduction ; Partitioning algorithms ; Physical Geography ; Physical Sciences ; Radar imaging ; Radar polarimetry ; Remote Sensing ; SAR (radar) ; Science & Technology ; superpixel ; Synthetic aperture radar ; synthetic aperture radar (SAR) ; Technology ; unsupervised segmentation ; weak edge</subject><ispartof>IEEE journal of selected topics in applied earth observations and remote sensing, 2020, Vol.13, p.1972-1985</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a superpixel boundary-based edge description algorithm (SpBED) is proposed. First, an edge detection method with three edge detectors is used. Therefore, accurate strong edges of SAR images can be extracted, and false edges that are easy to appear in a single detection method can be effectively eliminated. Then the weak edges of the image are extracted by superpixel generation algorithm. The extracted weak edges can supplement the edge information that is difficult to extract by edge detection. Superpixel boundaries are also used to carry the strong edges, so that the strong and weak edges can be completely represented by superpixel boundaries. Finally, boundary constraint superpixel smoothing is used to reduce the effects of noise, and k-means algorithm is performed on superpixels. Since edge information is carried by superpixels, it effectively guarantees the segmentation accuracy in edge region. Compared with seven state-of-the-art algorithms, segmentation results on simulated images and real images demonstrate the effectiveness of the proposed SpBED.</description><subject>Algorithms</subject><subject>Boundaries</subject><subject>Clustering algorithms</subject><subject>Computer simulation</subject><subject>Detection</subject><subject>Detectors</subject><subject>Edge detection</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Geography, Physical</subject><subject>Image edge detection</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Imaging Science & Photographic Technology</subject><subject>Linear programming</subject><subject>Methods</subject><subject>Noise reduction</subject><subject>Partitioning algorithms</subject><subject>Physical Geography</subject><subject>Physical Sciences</subject><subject>Radar imaging</subject><subject>Radar polarimetry</subject><subject>Remote Sensing</subject><subject>SAR (radar)</subject><subject>Science & Technology</subject><subject>superpixel</subject><subject>Synthetic aperture radar</subject><subject>synthetic aperture radar (SAR)</subject><subject>Technology</subject><subject>unsupervised segmentation</subject><subject>weak edge</subject><issn>1939-1404</issn><issn>2151-1535</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkc1u1DAURi0EEkPhCbqJxBJluP6L7eV0KGVQpUpNWVtOcj14NBMHJ6PC2-M0VdmysmWd890rf4RcUlhTCubz9_phc1-vGTBYM6NVJfkrsmJU0pJKLl-TFTXclFSAeEvejeMBoGLK8BW5q88DpiH8xmNxFc9959Kf8sqN2BXX3R6LLzi2KQxTiH2xOe5jCtPPU-FjKurNfbE7uczUuD9hP7kZek_eeHcc8cPzeUF-fL1-2H4rb-9udtvNbdkK0FNpoBPCtEqJpnNcA_NGcuqFFNpgh4o1FFE61aL3lResEb7hWWAVopCN5Bdkt-R20R3skMIpL26jC_bpIaa9dWkK7RFtI1wehpryHO-1016Ba0XO79A7MWd9XLKGFH-dcZzsIZ5Tn9e3TEClgHE-U3yh2hTHMaF_mUrBzi3YpQU7t2CfW8jWp8V6xCb6sQ3Yt_hiAoDkmrJK5BuoTOv_p7dh-fJtrm3K6uWiBsR_igEtuAH-FweypF8</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Shang, Ronghua</creator><creator>Lin, Junkai</creator><creator>Jiao, Licheng</creator><creator>Yang, Xiaohui</creator><creator>Li, Yangyang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Algorithms Boundaries Clustering algorithms Computer simulation Detection Detectors Edge detection Engineering Engineering, Electrical & Electronic Geography, Physical Image edge detection Image processing Image segmentation Imaging Science & Photographic Technology Linear programming Methods Noise reduction Partitioning algorithms Physical Geography Physical Sciences Radar imaging Radar polarimetry Remote Sensing SAR (radar) Science & Technology superpixel Synthetic aperture radar synthetic aperture radar (SAR) Technology unsupervised segmentation weak edge |
| title | Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation |
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