{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:10:41Z","timestamp":1780542641372,"version":"3.54.1"},"reference-count":22,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T00:00:00Z","timestamp":1639180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806163"],"award-info":[{"award-number":["61806163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703332"],"award-info":[{"award-number":["61703332"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Plan in the Shaanxi Province of China","doi-asserted-by":"publisher","award":["2019-SQ-359"],"award-info":[{"award-number":["2019-SQ-359"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Plan in the Shaanxi Province of China","doi-asserted-by":"publisher","award":["2019-SQ-520"],"award-info":[{"award-number":["2019-SQ-520"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Change detection from synthetic aperture radar (SAR) images is of great significance for natural environmental protection and human societal activity, which can be regarded as the process of assigning a class label (changed or unchanged) to each of the image pixels. This paper presents a novel classification technique to address the SAR change-detection task that employs a generalized Gamma deep belief network (g\u0393-DBN) to learn features from difference images. We aim to develop a robust change detection method that can adapt to different types of scenarios for bitemporal co-registered Yellow River SAR image data set. This data set characterized by different looks, which means that the two images are affected by different levels of speckle. Widely used probability distributions offer limited accuracy for describing the opposite class pixels of difference images, making change detection entail greater difficulties. To address the issue, first, a g\u0393-DBN can be constructed to extract the hierarchical features from raw data and fit the distribution of the difference images by means of a generalized Gamma distribution. Next, we propose learning the stacked spatial and temporal information extracted from various difference images by the g\u0393-DBN. Consequently, a joint high-level representation can be effectively learned for the final change map. The visual and quantitative analysis results obtained on the Yellow River SAR image data set demonstrate the effectiveness and robustness of the proposed method.<\/jats:p>","DOI":"10.3390\/s21248290","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T01:29:33Z","timestamp":1639358973000},"page":"8290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Change Detection in Synthetic Aperture Radar Images Based on a Generalized Gamma Deep Belief Networks"],"prefix":"10.3390","volume":"21","author":[{"given":"Meng","family":"Jia","sequence":"first","affiliation":[{"name":"Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi\u2019an University of Technology, NO. 5 South Jinhua Road, Xi\u2019an 710048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2475-7177","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi\u2019an University of Technology, NO. 5 South Jinhua Road, Xi\u2019an 710048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/LGRS.2011.2167211","article-title":"A Neighborhood-Based Ratio Approach for Change Detection in SAR Images","volume":"9","author":"Gong","year":"2012","journal-title":"IEEE Geosci. 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