{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:24:15Z","timestamp":1774495455942,"version":"3.50.1"},"reference-count":91,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42075130"],"award-info":[{"award-number":["42075130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.engappai.2024.108960","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T14:53:52Z","timestamp":1721228032000},"page":"108960","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"PB","title":["Multi-granularity siamese transformer-based change detection in remote sensing imagery"],"prefix":"10.1016","volume":"136","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2044-3311","authenticated-orcid":false,"given":"Lei","family":"Song","sequence":"first","affiliation":[]},{"given":"Min","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Yao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Liguo","family":"Weng","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3835-6075","authenticated-orcid":false,"given":"Haifeng","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Qian","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2024.108960_b1","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.3390\/rs15071860","article-title":"Transformers in remote sensing: A survey","volume":"15","author":"Aleissaee","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b2","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/10095020.2022.2085633","article-title":"Deep learning for change detection in remote sensing: a review","volume":"26","author":"Bai","year":"2023","journal-title":"Geo-Spatial Inf. Sci."},{"key":"10.1016\/j.engappai.2024.108960_b3","series-title":"Yolov4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"key":"10.1016\/j.engappai.2024.108960_b4","series-title":"Video super-resolution transformer","author":"Cao","year":"2021"},{"key":"10.1016\/j.engappai.2024.108960_b5","series-title":"European Conference on Computer Vision","first-page":"213","article-title":"End-to-end object detection with transformers","author":"Carion","year":"2020"},{"key":"10.1016\/j.engappai.2024.108960_b6","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/TGRS.2009.2029095","article-title":"Unsupervised change detection for satellite images using dual-tree complex wavelet transform","volume":"48","author":"Celik","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z., 2020a. Dynamic convolution: Attention over convolution kernels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11030\u201311039.","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"10.1016\/j.engappai.2024.108960_b8","doi-asserted-by":"crossref","first-page":"4853","DOI":"10.3390\/rs15194853","article-title":"Msfanet: Multi-scale strip feature attention network for cloud and cloud shadow segmentation","volume":"15","author":"Chen","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b9","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2024.108960_b10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b11","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.3390\/rs12101662","article-title":"A spatial-temporal attention-based method and a new dataset for remote sensing image change detection","volume":"12","author":"Chen","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b12","doi-asserted-by":"crossref","first-page":"5874","DOI":"10.1080\/01431161.2022.2073795","article-title":"Manet: a multi-level aggregation network for semantic segmentation of high-resolution remote sensing images","volume":"43","author":"Chen","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b13","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.3390\/rs15061536","article-title":"Double branch parallel network for segmentation of buildings and waters in remote sensing images","volume":"15","author":"Chen","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b14","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"Dasnet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b15","series-title":"Conditional positional encodings for vision transformers","author":"Chu","year":"2021"},{"key":"10.1016\/j.engappai.2024.108960_b16","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1016\/j.foreco.2010.03.008","article-title":"Assessing changes in forest fragmentation following infestation using time series landsat imagery","volume":"259","author":"Coops","year":"2010","journal-title":"Forest Ecol. Manag."},{"key":"10.1016\/j.engappai.2024.108960_b17","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jiang, C., Wang, L., Wu, G., 2022. Mixformer: End-to-end tracking with iterative mixed attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 13608\u201313618.","DOI":"10.1109\/CVPR52688.2022.01324"},{"key":"10.1016\/j.engappai.2024.108960_b18","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.3390\/rs15164005","article-title":"Lpmsnet: Location pooling multi-scale network for cloud and cloud shadow segmentation","volume":"15","author":"Dai","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b19","series-title":"2018 25th IEEE International Conference on Image Processing","first-page":"4063","article-title":"Fully convolutional siamese networks for change detection","author":"Daudt","year":"2018"},{"key":"10.1016\/j.engappai.2024.108960_b20","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"4055","article-title":"Mas-net: Mixed-feature attention siamese network for change detection on remote sensing images","author":"Ding","year":"2024"},{"key":"10.1016\/j.engappai.2024.108960_b21","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3390\/rs16010112","article-title":"Multi-level attention interactive network for cloud and snow detection segmentation","volume":"16","author":"Ding","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b22","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.engappai.2024.108960_b23","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TMM.2020.2975961","article-title":"Spa-gan: Spatial attention gan for image-to-image translation","volume":"23","author":"Emami","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.engappai.2024.108960_b24","first-page":"1","article-title":"Snunet-cd: A densely connected siamese network for change detection of vhr images","volume":"19","author":"Fang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2024.108960_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.103609","article-title":"Purifying real images with an attention-guided style transfer network for gaze estimation","volume":"91","author":"Fu","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2024.108960_b26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.16.016513","article-title":"MLNet: multichannel feature fusion lozenge network for land segmentation","volume":"16","author":"Gao","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b27","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2024.108960_b28","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., Ukita, N., 2018. Deep back-projection networks for super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1664\u20131673.","DOI":"10.1109\/CVPR.2018.00179"},{"key":"10.1016\/j.engappai.2024.108960_b29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.engappai.2024.108960_b30","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TGRS.2019.2948659","article-title":"From w-net to cdgan: Bitemporal change detection via deep learning techniques","volume":"58","author":"Hou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b31","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10.1016\/j.engappai.2024.108960_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106196","article-title":"A multi-stage underwater image aesthetic enhancement algorithm based on a generative adversarial network","volume":"123","author":"Hu","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2024.108960_b33","doi-asserted-by":"crossref","first-page":"6762","DOI":"10.1109\/JSTARS.2024.3374233","article-title":"Hycloudx: A multi-branch hybrid segmentation network with band fusion for cloud\/shadow","volume":"17","author":"Hu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b34","doi-asserted-by":"crossref","first-page":"247","DOI":"10.3390\/ijgi12060247","article-title":"Multi-supervised feature fusion attention network for clouds and shadows detection","volume":"12","author":"Ji","year":"2023","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"10.1016\/j.engappai.2024.108960_b35","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.3390\/rs16081387","article-title":"Mdanet: A high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion","volume":"16","author":"Jiang","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b36","doi-asserted-by":"crossref","unstructured":"Khan, J., Kim, K., 2022. An efficient cnn-based automated leukemia diagnosis using microscopic blood smear images and subtypes classification. In: Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference. pp. 111\u2013116.","DOI":"10.1145\/3582099.3582117"},{"key":"10.1016\/j.engappai.2024.108960_b37","article-title":"A higher prediction accuracy\u2013based alpha\u2013beta filter algorithm using the feedforward artificial neural network","author":"Khan","year":"2022","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.engappai.2024.108960_b38","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2007.02.010","article-title":"Early fire detection using non-linear multitemporal prediction of thermal imagery","volume":"110","author":"Koltunov","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.engappai.2024.108960_b39","article-title":"Change detection in remote sensing images using conditional adversarial networks","volume":"42","author":"Lebedev","year":"2018","journal-title":"Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci."},{"key":"10.1016\/j.engappai.2024.108960_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.103819","article-title":"Change detection in images using shape-aware siamese convolutional network","volume":"94","author":"Li","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2024.108960_b41","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.3390\/rs16101665","article-title":"Multi-scale fusion siamese network based on three-branch attention mechanism for high-resolution remote sensing image change detection","volume":"16","author":"Li","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b42","first-page":"35","article-title":"Change detection for high-resolution remote sensing images based on a unet-like siamese-structured transformer network","author":"Liang","year":"2023","journal-title":"Sensors Mater."},{"key":"10.1016\/j.engappai.2024.108960_b43","doi-asserted-by":"crossref","DOI":"10.1109\/JSTARS.2023.3278726","article-title":"Enhanced self-attention network for remote sensing building change detection","author":"Liang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b44","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.engappai.2024.108960_b45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S., 2022. A convnet for the 2020. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11976\u201311986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10.1016\/j.engappai.2024.108960_b46","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model","volume":"18","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2024.108960_b47","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, W., Tang, Y., Tang, J., Wu, G., 2020b. Residual feature aggregation network for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2359\u20132368.","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"10.1016\/j.engappai.2024.108960_b48","doi-asserted-by":"crossref","unstructured":"Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., Zeng, T., 2022a. Transformer for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 457\u2013466.","DOI":"10.1109\/CVPRW56347.2022.00061"},{"key":"10.1016\/j.engappai.2024.108960_b49","first-page":"1","article-title":"Dual-branch network for cloud and cloud shadow segmentation","volume":"60","author":"Lu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b50","first-page":"1","article-title":"Simple multiscale unet for change detection with heterogeneous remote sensing images","volume":"19","author":"Lv","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2024.108960_b51","article-title":"Multi-scale attention network guided with change gradient image for land cover change detection using remote sensing images","author":"Lv","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2024.108960_b52","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1080\/01431161.2023.2190471","article-title":"Fenet: feature enhancement network for land cover classification","volume":"44","author":"Ma","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b53","unstructured":"Malila, W.A., 1980. Change vector analysis: An approach for detecting forest changes with landsat. In: LARS Symposia. p. 385."},{"key":"10.1016\/j.engappai.2024.108960_b54","doi-asserted-by":"crossref","first-page":"5940","DOI":"10.1080\/01431161.2021.2014077","article-title":"Cloud\/shadow segmentation based on multi-level feature enhanced network for remote sensing imagery","volume":"43","author":"Miao","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b55","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TIP.2006.888195","article-title":"The regularized iteratively reweighted mad method for change detection in multi-and hyperspectral data","volume":"16","author":"Nielsen","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2024.108960_b56","series-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII","first-page":"368","article-title":"Change detection in hyperspectral imagery using temporal principal components","author":"Ortiz-Rivera","year":"2006"},{"key":"10.1016\/j.engappai.2024.108960_b57","doi-asserted-by":"crossref","first-page":"5891","DOI":"10.1109\/TGRS.2020.3011913","article-title":"Semicdnet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b58","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.3390\/rs11111382","article-title":"End-to-end change detection for high resolution satellite images using improved unet++","volume":"11","author":"Peng","year":"2019","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2021.104940","article-title":"Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow","volume":"157","author":"Qu","year":"2021","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.engappai.2024.108960_b60","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.3390\/rs16071269","article-title":"Mfinet: Multi-scale feature interaction network for change detection of high-resolution remote sensing images","volume":"16","author":"Ren","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b61","doi-asserted-by":"crossref","first-page":"4899","DOI":"10.1109\/JSTARS.2024.3362370","article-title":"Dual attention-guided multiscale feature aggregation network for remote sensing image change detection","volume":"17","author":"Ren","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b62","series-title":"The Principles of Deep Learning Theory","author":"Roberts","year":"2022"},{"key":"10.1016\/j.engappai.2024.108960_b63","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.engappai.2024.108960_b64","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"421","article-title":"Concurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks","author":"Roy","year":"2018"},{"key":"10.1016\/j.engappai.2024.108960_b65","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.apgeog.2006.09.004","article-title":"Remote sensing and gis for mapping and monitoring land cover and land-use changes in the northwestern coastal zone of egypt","volume":"27","author":"Shalaby","year":"2007","journal-title":"Appl. Geogr."},{"key":"10.1016\/j.engappai.2024.108960_b66","doi-asserted-by":"crossref","first-page":"8442","DOI":"10.1109\/JSTARS.2022.3204191","article-title":"Pstnet: Progressive sampling transformer network for remote sensing image change detection","volume":"15","author":"Song","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b67","article-title":"Suacdnet: Attentional change detection network based on siamese u-shaped structure","volume":"105","author":"Song","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.engappai.2024.108960_b68","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/01431160802261163","article-title":"Three decades of land use variations in mexico city","volume":"30","author":"Torres-Vera","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b69","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2024.108960_b70","doi-asserted-by":"crossref","first-page":"2228","DOI":"10.3390\/rs14092228","article-title":"A network combining a transformer and a convolutional neural network for remote sensing image change detection","volume":"14","author":"Wang","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b71","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105504","article-title":"Banet: Small and multi-object detection with a bidirectional attention network for traffic scenes","volume":"117","author":"Wang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2024.108960_b72","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.3390\/rs15092237","article-title":"Mbcnet: Multi-branch collaborative change-detection network based on siamese structure","volume":"15","author":"Wang","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b73","series-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13","article-title":"Supplementary material for \u201ceca-net: Efficient channel attention for deep convolutional neural networks","author":"Wang","year":"2020"},{"key":"10.1016\/j.engappai.2024.108960_b74","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1109\/JSTARS.2023.3347595","article-title":"Dual encoder-decoder network for land cover segmentation of remote sensing image","volume":"17","author":"Wang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b75","doi-asserted-by":"crossref","first-page":"6812","DOI":"10.1109\/JSTARS.2023.3295729","article-title":"Sgformer: A local and global features coupling network for semantic segmentation of land cover","volume":"16","author":"Weng","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b76","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S., 2018. Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.engappai.2024.108960_b77","doi-asserted-by":"crossref","DOI":"10.1049\/ipr2.13037","article-title":"A hybrid u-shaped and transformer network for change detection in high-resolution remote sensing images","author":"Wu","year":"2024","journal-title":"IET Image Process."},{"key":"10.1016\/j.engappai.2024.108960_b78","first-page":"12077","article-title":"Segformer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2024.108960_b79","doi-asserted-by":"crossref","DOI":"10.1109\/JSTARS.2024.3350044","article-title":"Mask guided local-global attentive network for change detection in remote sensing images","author":"Xiong","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b80","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.3390\/rs15092406","article-title":"A cnn-transformer network combining cbam for change detection in high-resolution remote sensing images","volume":"15","author":"Yin","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b81","article-title":"Attention-guided siamese networks for change detection in high resolution remote sensing images","volume":"117","author":"Yin","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.engappai.2024.108960_b82","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.3390\/rs16101765","article-title":"Amfnet: Attention-guided multi-scale fusion network for bi-temporal change detection in remote sensing images","volume":"16","author":"Zhan","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b83","doi-asserted-by":"crossref","first-page":"880","DOI":"10.3390\/rs16050880","article-title":"An efficient hybrid cnn-transformer approach for remote sensing super-resolution","volume":"16","author":"Zhang","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3618342","article-title":"A unified arbitrary style transfer framework via adaptive contrastive learning","volume":"42","author":"Zhang","year":"2023","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.engappai.2024.108960_b85","article-title":"Crsnet: Cloud and cloud shadow refinement segmentation networks for remote sensing imagery","volume":"15","author":"Zhang","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b86","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/LGRS.2018.2869608","article-title":"Triplet-based semantic relation learning for aerial remote sensing image change detection","volume":"16","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.engappai.2024.108960_b87","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b88","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al., 2021a. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 6881\u20136890.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"10.1016\/j.engappai.2024.108960_b89","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2021.03.005","article-title":"Clnet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery","volume":"175","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.engappai.2024.108960_b90","series-title":"Deepvit: Towards deeper vision transformer","author":"Zhou","year":"2021"},{"key":"10.1016\/j.engappai.2024.108960_b91","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197624011187?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197624011187?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T20:21:54Z","timestamp":1738441314000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197624011187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":91,"alternative-id":["S0952197624011187"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2024.108960","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-granularity siamese transformer-based change detection in remote sensing imagery","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2024.108960","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108960"}}