{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:21:55Z","timestamp":1777890115789,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MGS"],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore, the segmentation process can not directly rely on the intensity information alone, but it must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use supervised information about regions for segmentation criteria in which ANN is employed to give training on the basis of known ground truth image derived. Three different features are employed for segmentation, first feature is the original image, second feature is the roughness information and the third feature is the filtered image. The segmentation accuracy is measured against the Difficulty of Segmentation (DoS) and Cross Region Fitting (CRF) methods. The performance of our algorithm has been compared with other proposed methods employing the same set of data.<\/jats:p>","DOI":"10.3233\/mgs-200337","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T18:48:08Z","timestamp":1609872488000},"page":"397-408","source":"Crossref","is-referenced-by-count":1,"title":["Synthetic aperture radar image segmentation using supervised artificial neural network"],"prefix":"10.1177","volume":"16","author":[{"given":"R.","family":"Lalchhanhima","sequence":"first","affiliation":[{"name":"Department of Information Technology, Mizoram University, India"},{"name":"Department of Information Technology, NEHU, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Goutam","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Information Technology, NEHU, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morrel V.L.","family":"Nunsanga","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Mizoram University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debdatta","family":"Kandar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, NEHU, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/MGS-200337_ref2","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/PROC.1978.10961","article-title":"Tutorial review of synthetic-aperture radar (sar) with applications to imaging of the ocean surface","volume":"66","author":"Tomiyasu","year":"1978","journal-title":"Proceedings of the IEEE"},{"issue":"7","key":"10.3233\/MGS-200337_ref4","doi-asserted-by":"crossref","first-page":"4440","DOI":"10.1109\/TGRS.2013.2282036","article-title":"Mean-shift-based speckle filtering of polarimetric sar data","volume":"52","author":"Lang","year":"2014","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"1","key":"10.3233\/MGS-200337_ref5","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/LGRS.2009.2028588","article-title":"Sar image despeckling using edge detection and feature clustering in bandelet domain","volume":"7","author":"Zhang","year":"2010","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"11","key":"10.3233\/MGS-200337_ref6","doi-asserted-by":"crossref","first-page":"7222","DOI":"10.1109\/TGRS.2014.2309725","article-title":"A modified level set approach for segmentation of multiband polarimetric sar images","volume":"52","author":"Yin","year":"2014","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.3233\/MGS-200337_ref7","doi-asserted-by":"crossref","unstructured":"A. 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