{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:46:14Z","timestamp":1760147174489,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"LuTan-1 L-Band Spaceborne Bistatic SAR data processing program","award":["E0H2080702","2019YJ028"],"award-info":[{"award-number":["E0H2080702","2019YJ028"]}]},{"DOI":"10.13039\/501100015282","name":"China Academy of Railway Sciences Fund","doi-asserted-by":"publisher","award":["E0H2080702","2019YJ028"],"award-info":[{"award-number":["E0H2080702","2019YJ028"]}],"id":[{"id":"10.13039\/501100015282","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar (SAR) image change detection is one of the most important applications in remote sensing. Before performing change detection, the original SAR image is often cropped to extract the region of interest (ROI). However, the size of the ROI often affects the change detection results. Therefore, it is necessary to detect changes using local information. This paper proposes a novel unsupervised change detection framework based on deep learning. The specific method steps are described as follows: First, we use histogram fitting error minimization (HFEM) to perform thresholding for a difference image (DI). Then, the DI is fed into a convolutional neural network (CNN). Therefore, the proposed method is called HFEM-CNN. We test three different CNN architectures called Unet, PSPNet and the designed fully convolutional neural network (FCNN) for the framework. The overall loss function is a weighted average of pixel loss and neighborhood loss. The weight between pixel loss and neighborhood loss is determined by the manually set parameter \u03bb. Compared to other recently proposed methods, HFEM-CNN does not need a fragment removal procedure as post-processing. This paper conducts experiments for water and building change detection on three datasets. The experiments are divided into two parts: whole data experiments and random cropped data experiments. The complete experiments prove that the performance of the method in this paper is close to other methods on complete datasets. The random cropped data experiment is to perform local change detection using patches cropped from the whole datasets. The proposed method is slightly better than traditional methods in the whole data experiments. In experiments with randomly cropped data, the average kappa coefficient of our method on 63 patches is over 3.16% compared to other methods. Experiments also show that the proposed method is suitable for local change detection and robust to randomness and choice of hyperparameters.<\/jats:p>","DOI":"10.3390\/rs15020470","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:29:57Z","timestamp":1673576997000},"page":"470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised SAR Image Change Detection Based on Histogram Fitting Error Minimization and Convolutional Neural Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2413-6370","authenticated-orcid":false,"given":"Kaiyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6991-583X","authenticated-orcid":false,"given":"Xiaolei","family":"Lv","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Capital International Airport Group, Beijing Daxing International Airport, Beijing 102604, China"}]},{"given":"Huiming","family":"Chai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TGRS.2004.842441","article-title":"An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. 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