{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:43:56Z","timestamp":1764225836039,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"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":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"],"award-info":[{"award-number":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"],"award-info":[{"award-number":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"],"award-info":[{"award-number":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"],"award-info":[{"award-number":["62101081","KJZD-M202000702 and KJQN202100747","2019ZD022","2019GG0138, 2019GG139, and 2020GG0073"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder\u2013decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data.<\/jats:p>","DOI":"10.3390\/rs14246361","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T02:54:02Z","timestamp":1671159242000},"page":"6361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Cascaded U-Net with Training Wheel Attention Module for Change Detection in Satellite Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2826-9987","authenticated-orcid":false,"given":"Elyar","family":"Adil","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400000, China"}]},{"given":"Xiangli","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400000, China"}]},{"given":"Pingping","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1541-6992","authenticated-orcid":false,"given":"Xiaolong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9071-9470","authenticated-orcid":false,"given":"Weixian","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"given":"Jianxi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s11069-015-1595-z","article-title":"Detection of tsunami-induced changes using generalized improved fuzzy radial basis function neural network","volume":"77","author":"Mehrotra","year":"2015","journal-title":"Nat. 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