{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:29:39Z","timestamp":1770226179746,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:00:00Z","timestamp":1667606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Funds for the Central Universities","award":["JB211312"],"award-info":[{"award-number":["JB211312"]}]},{"name":"Research Funds for the Central Universities","award":["XJS221307"],"award-info":[{"award-number":["XJS221307"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The generation of superpixels is becoming a critical step in SAR image segmentation. However, most studies on superpixels only focused on clustering methods without considering multi-feature in SAR images. Generating superpixels for complex scenes is a challenging task. It is also time consuming and inconvenient to manually adjust the parameters to regularize the shapes of superpixels. To address these issues, we propose a new superpixel generation method for SAR images based on edge detection and texture region selection (EDTRS), which takes into account the different features of SAR images. Firstly, a Gaussian function is applied in the neighborhood of each pixel in eight directions, and a Sobel operator is used to determine the redefined region. Then, 2D entropy is introduced to adjust the edge map. Secondly, local outlier factor (LOF) detection is used to eliminate speckle-noise interference in SAR images. We judge whether the texture has periodicity and introduce an edge map to select the appropriate region and extract texture features for the target pixel. A gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) are combined to extract texture features. Finally, we use a novel approach to combine the features extracted, and the pixels are clustered by the K-means method. Experimental results with different SAR images show that the proposed method outperforms existing superpixel generation methods with an increase of 5\u201310% in accuracy and produces more regular shapes.<\/jats:p>","DOI":"10.3390\/rs14215589","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T03:02:22Z","timestamp":1667790142000},"page":"5589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection"],"prefix":"10.3390","volume":"14","author":[{"given":"Hang","family":"Yu","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0021-3447","authenticated-orcid":false,"given":"Haoran","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2794-3557","authenticated-orcid":false,"given":"Zhiheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]},{"given":"Suiping","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]},{"given":"Xiangjie","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shang, R., Peng, P., Shang, F., Jiao, L., Shen, Y., and Stolkin, R. 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