{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:03:10Z","timestamp":1777705390602,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,2,14]]},"abstract":"<jats:p>Convolutional neural networks (CNNs) have received significant attention for change detection (CD) on multimodal remote sensing images, but they struggle to capture global cues due to the locality of convolution operations. In contrast, the transformer can learn global semantic information by dividing the input image into patches, adding position encodings, and utilizing the self-attention mechanism. Motivated by this, we propose mSwinUNet, a novel end-to-end multi-modal model with swin-transformer-based and U-shaped siamese network architectures for supervised CD using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Imager (MSI) data. mSwinUNet contains multi-modal encoder with difference module, bottleneck, and fused decoder, and all of them are based on swin transformer. Firstly, tokenized multi-modal bitemporal image patches are fed into multiple Siamese encoder branches to extract multi-level multi-modal difference feature maps in parallel. Subsequently, the last level multi-modal difference maps are fused to generate the smallest scale change map in the bottleneck. Then, the hierarchical decoder incorporates patch expansion and fusion operations to fuse multi-scale difference and change maps, effectively recuperating the details of the change information. Finally, the last patch expansion and a linear projection are applied to output the final change map, which preserves the identical spatial resolution as the input image. Extensive experiments have shown that mSwinUNet outperforms several the state-of-the-art multi-modal CD methods on OSCD dataset and the corresponding Sentinel-1 SAR data.<\/jats:p>","DOI":"10.3233\/jifs-233868","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T13:34:49Z","timestamp":1706016889000},"page":"4243-4252","source":"Crossref","is-referenced-by-count":3,"title":["mSwinUNet: A multi-modal U-shaped swin transformer for supervised change detection"],"prefix":"10.1177","volume":"46","author":[{"given":"Tianjun","family":"Lu","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luo","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/JIFS-233868_ref2","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","article-title":"Earthquake damage assessmentof buildings using vhr optical and sar imagery","volume":"48","author":"Brunner","year":"2010","journal-title":"IEEETransactions on Geoscience and Remote Sensing"},{"key":"10.3233\/JIFS-233868_ref4","first-page":"1","article-title":"Remote sensing image change detectionwith transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Transactions on Geoscience and RemoteSensing"},{"key":"10.3233\/JIFS-233868_ref5","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"Dasnet: Dual attentive fully convolutional siamese networksfor change detection in high-resolution satellite images","volume":"14","author":"Chen","year":"2020","journal-title":"IEEEJournal of Selected Topics in Applied Earth Observations and RemoteSensing"},{"issue":"12","key":"10.3233\/JIFS-233868_ref6","doi-asserted-by":"crossref","first-page":"12575","DOI":"10.3390\/rs61212575","article-title":"The influence of polarimetricparameters and an object-based approach on land cover classificationin coastal wetlands","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sensing"},{"key":"10.3233\/JIFS-233868_ref9","first-page":"243","article-title":"Fusing multi-modal data forsupervised change detection","volume":"43","author":"Ebel","year":"2021","journal-title":"The International Archives of thePhotogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"10.3233\/JIFS-233868_ref10","first-page":"1","article-title":"Snunet-cd: A densely connectedsiamese network for change detection of vhr images","volume":"19","author":"Fang","year":"2021","journal-title":"IEEEGeoscience and Remote Sensing Letters"},{"key":"10.3233\/JIFS-233868_ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3119856","article-title":"Sentinel-1 andsentinel-2 data fusion for urban change detection using a dualstream u-net","volume":"19","author":"Hafner","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"7","key":"10.3233\/JIFS-233868_ref13","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1016\/j.rse.2008.07.018","article-title":"Remote sensing change detection tools fornatural resource managers: Understanding concepts and tradeoffs inthe design of landscape monitoring projects","volume":"113","author":"Kennedy","year":"2009","journal-title":"Remote Sensing ofEnvironment"},{"key":"10.3233\/JIFS-233868_ref14","doi-asserted-by":"crossref","unstructured":"Liang C. , Chen P. , Liu H. , Zhu X. , Geng Y. and Zhang Z. , Changedetection for high-resolution remote sensing images based on aunet-like siamese-structured transformer network, Sensors &Materials 35 (2023).","DOI":"10.18494\/SAM4180"},{"issue":"3","key":"10.3233\/JIFS-233868_ref15","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TNNLS.2018.2847309","article-title":"Localrestricted convolutional neural network for change detection inpolarimetric sar images","volume":"30","author":"Liu","year":"2018","journal-title":"IEEE transactions on neural networksand learning systems"},{"key":"10.3233\/JIFS-233868_ref16","first-page":"1","article-title":"Learning token-alignedrepresentations with multimodel transformers fordifferent-resolution change detection","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Transactions onGeoscience and Remote Sensing"},{"issue":"5","key":"10.3233\/JIFS-233868_ref17","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building changedetection for remote sensing images using a dual-task constraineddeep siamese convolutional network model","volume":"18","author":"Liu","year":"2020","journal-title":"IEEE Geoscience andRemote Sensing Letters"},{"issue":"4","key":"10.3233\/JIFS-233868_ref18","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TIP.2017.2784560","article-title":"Change detection inheterogenous remote sensing images via homogeneous pixeltransformation","volume":"27","author":"Liu","year":"2017","journal-title":"IEEE Transactions on Image Processing"},{"issue":"5","key":"10.3233\/JIFS-233868_ref20","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/JSTARS.2018.2803784","article-title":"Landslide inventorymapping from bitemporal high-resolution remote sensing images usingchange detection and multiscale segmentation","volume":"11","author":"Lv","year":"2018","journal-title":"IEEE Journal ofSelected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"9","key":"10.3233\/JIFS-233868_ref22","doi-asserted-by":"crossref","first-page":"7296","DOI":"10.1109\/TGRS.2020.3033009","article-title":"Optical remote sensing imagechange detection based on attention mechanism and image difference","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"8","key":"10.3233\/JIFS-233868_ref24","doi-asserted-by":"crossref","first-page":"3430","DOI":"10.1109\/JSTARS.2016.2542074","article-title":"Building change detection using highresolution remotely sensed data and gis","volume":"9","author":"Sofina","year":"2016","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"10","key":"10.3233\/JIFS-233868_ref28","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change detectionbased on deep siamese convolutional network for optical aerialimages","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"10.3233\/JIFS-233868_ref29","first-page":"1","article-title":"Swinsunet: Pure transformernetwork for remote sensing image change detection","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEETransactions on Geoscience and Remote Sensing"},{"issue":"10","key":"10.3233\/JIFS-233868_ref30","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.1109\/TGRS.2020.2981051","article-title":"A feature difference convolutional neuralnetwork-based change detection method","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Transactions onGeoscience and Remote Sensing"},{"issue":"2","key":"10.3233\/JIFS-233868_ref31","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/LGRS.2018.2869608","article-title":"Triplet-based semanticrelation learning for aerial remote sensing image change detection","volume":"16","author":"Zhang","year":"2018","journal-title":"IEEE Geoscience and Remote Sensing Letters"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-233868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:42Z","timestamp":1777455822000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-233868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,14]]},"references-count":20,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-233868","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,14]]}}}