{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:15:10Z","timestamp":1774120510167,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology","award":["TKL2023A08"],"award-info":[{"award-number":["TKL2023A08"]}]},{"name":"Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology","award":["22YBA012"],"award-info":[{"award-number":["22YBA012"]}]},{"name":"Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology","award":["2022JJ30698"],"award-info":[{"award-number":["2022JJ30698"]}]},{"name":"Hunan Social Science Foundation","award":["TKL2023A08"],"award-info":[{"award-number":["TKL2023A08"]}]},{"name":"Hunan Social Science Foundation","award":["22YBA012"],"award-info":[{"award-number":["22YBA012"]}]},{"name":"Hunan Social Science Foundation","award":["2022JJ30698"],"award-info":[{"award-number":["2022JJ30698"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["TKL2023A08"],"award-info":[{"award-number":["TKL2023A08"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["22YBA012"],"award-info":[{"award-number":["22YBA012"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2022JJ30698"],"award-info":[{"award-number":["2022JJ30698"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The existing change detection (CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is essentially dependent on change regions of interest (CRoIs), meaning that the CD results are directly determined by the semantics changes in interest, making its primary image factor semantic of interest rather than visual. The ViFi-CD paradigm can only assign specific semantics of interest to specific change features extracted from visual differences, leading to the inevitable omission of potential CRoIs and the inability to adapt to different CRoI CD tasks. In other words, changes in other CRoIs cannot be detected by the ViFi-CD method without retraining the model or significantly modifying the method. This paper introduces a new CD paradigm, the semantic-first CD (SeFi-CD) paradigm. The core idea of SeFi-CD is to first perceive the dynamic semantics of interest and then visually search for change features related to the semantics. Based on the SeFi-CD paradigm, we designed Anything You Want Change Detection (AUWCD). Experiments on public datasets demonstrate that the AUWCD outperforms the current state-of-the-art CD methods, achieving an average F1 score 5.01% higher than that of these advanced supervised baselines on the SECOND dataset, with a maximum increase of 13.17%. The proposed SeFi-CD offers a novel CD perspective and approach.<\/jats:p>","DOI":"10.3390\/rs16214109","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T09:52:54Z","timestamp":1730713974000},"page":"4109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6103-1113","authenticated-orcid":false,"given":"Ling","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Zhenyang","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Yipeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Beidou Operation Service Center, Sinopec Geophysical Corporation, Nanjing 211100, China"}]},{"given":"Chengli","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Jun","family":"Gan","sequence":"additional","affiliation":[{"name":"China Railway Design Corporation, Tianjin 300308, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1173-6593","authenticated-orcid":false,"given":"Haifeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Chao","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronic Infromation, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TGRS.2007.895835","article-title":"A split-based approach to unsupervised change detection in large-size multitemporal images: Application to tsunami-damage assessment","volume":"45","author":"Bovolo","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Coppin, P., Lambin, E., Jonckheere, I., and Muys, B. (2002). Digital Change Detection Methods in Natural Ecosystem Monitoring: A Review. Analysis of Multi-Temporal Remote Sensing Images, University of Trento.","DOI":"10.1142\/9789812777249_0001"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.landusepol.2006.02.002","article-title":"Corine land cover change detection in Europe (case studies of the Netherlands and Slovakia)","volume":"24","author":"Feranec","year":"2007","journal-title":"Land Use Policy"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Viana, C.M., Oliveira, S., Oliveira, S.C., and Rocha, J. (2019). Land use\/land cover change detection and urban sprawl analysis. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-815226-3.00029-6"},{"key":"ref_5","first-page":"1","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12145-019-00380-5","article-title":"Change detection techniques for remote sensing applications: A survey","volume":"12","author":"Asokan","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MGRS.2021.3088865","article-title":"Land cover change detection techniques: Very-high-resolution optical images: A review","volume":"10","author":"Lv","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","unstructured":"Malila, W.A. (1980). Change vector analysis: An approach for detecting forest changes with Landsat. LARS Symposia, Institute of Electrical and Electronics Engineers."},{"key":"ref_9","first-page":"19820045797","article-title":"Detecting residential land-use development at the urban fringe","volume":"48","author":"Jensen","year":"1982","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1080\/01431168108948362","article-title":"Procedures for change detection using Landsat digital data","volume":"2","author":"Howarth","year":"1981","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27442","DOI":"10.1109\/ACCESS.2018.2807380","article-title":"Adaptive change detection with significance test","volume":"6","author":"Ke","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TGRS.2015.2396686","article-title":"Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images","volume":"53","author":"Liu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/TGRS.2017.2765348","article-title":"Detecting changes between optical images of different spatial and spectral resolutions: A fusion-based approach","volume":"56","author":"Ferraris","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1080\/01431161.2011.572093","article-title":"A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images","volume":"3","author":"Xiong","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1109\/LGRS.2015.2433134","article-title":"A generalized likelihood ratio test for coherent change detection in polarimetric SAR","volume":"12","author":"Barber","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s12517-015-2301-x","article-title":"Design and implementation of an expert system for updating thematic maps using satellite imagery (case study: Changes of Lake Urmia)","volume":"9","author":"Sadeghi","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1080\/01431161.2017.1390272","article-title":"Fast detection of significantly transformed areas due to illegal waste burial with a procedure applicable to Landsat images","volume":"39","author":"Massarelli","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s12517-015-2267-8","article-title":"An effective hybrid classification approach using tasseled cap transformation (TCT) for improving classification of land use\/land cover (LU\/LC) in semi-arid region: A case study of Morva-Hadaf watershed, Gujarat, India","volume":"9","author":"Thakkar","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Solano-Correa, Y.T., Bovolo, F., and Bruzzone, L. (2018). An approach for unsupervised change detection in multitemporal VHR images acquired by different multispectral sensors. Remote Sens., 10.","DOI":"10.3390\/rs10040533"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"016016","DOI":"10.1117\/1.JRS.11.016016","article-title":"Applying the chi-square transformation and automatic secant thresholding to Landsat imagery as unsupervised change detection methods","volume":"11","author":"Novillo","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4914","DOI":"10.1080\/01431161.2017.1331475","article-title":"An approach based on discrete wavelet transform to unsupervised change detection in multispectral images","volume":"38","author":"Zhuang","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TNNLS.2016.2636227","article-title":"A deep convolutional coupling network for change detection based on heterogeneous optical and radar images","volume":"29","author":"Liu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TGRS.2017.2743243","article-title":"Learning multiscale deep features for high-resolution satellite image scene classification","volume":"56","author":"Liu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.rse.2018.06.031","article-title":"A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems","volume":"216","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ren, Q., Geng, J., Ding, M., and Li, J. (2018). Efficient patch-wise semantic segmentation for large-scale remote sensing images. Sensors, 18.","DOI":"10.3390\/s18103232"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TIP.2017.2784560","article-title":"Change detection in heterogenous remote sensing images via homogeneous pixel transformation","volume":"27","author":"Liu","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126385","DOI":"10.1109\/ACCESS.2020.3008036","article-title":"Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis","volume":"8","author":"Khelifi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, H., Peng, M., Zhong, Y., Xie, H., Hao, Z., Lin, J., Ma, X., and Hu, X. (2022). A survey on deep learning-based change detection from high-resolution remote sensing images. Remote Sens., 14.","DOI":"10.3390\/rs14071552"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_30","unstructured":"Daudt, R.C., Le Saux, B., and Boulch, A. (2018, January 7\u201310). Fully convolutional siamese networks for change detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, Athens, Greece."},{"key":"ref_31","unstructured":"Guo, E., Fu, X., Zhu, J., Deng, M., Liu, Y., Zhu, Q., and Li, H. (2018). Learning to measure change: Fully convolutional siamese metric networks for scene change detection. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.neucom.2021.06.059","article-title":"Fully convolutional siamese networks based change detection for optical aerial images with focal contrastive loss","volume":"457","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_33","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":"ref_34","first-page":"5604816","article-title":"A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection","volume":"60","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"102348","article-title":"ADS-Net:An Attention-Based deeply supervised network for remote sensing image change detection","volume":"101","author":"Wang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_37","first-page":"8007805","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":"ref_38","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"(Comput. Intell Neurosci., 2022). A Transformer-Based Network for Change Detection in Remote Sensing Using Multiscale Difference- Enhancement, Comput. Intell Neurosci.","DOI":"10.1155\/2022\/2189176"},{"key":"ref_40","first-page":"5607514","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"5622519","article-title":"TransUNetCD: A hybrid transformer network for change detection in optical remote-sensing images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"41409","DOI":"10.1364\/OE.440720","article-title":"TransCD: Scene change detection via transformer-based architecture","volume":"29","author":"Wang","year":"2021","journal-title":"Optics Express"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5224713","DOI":"10.1109\/TGRS.2022.3221492","article-title":"SwinSUNet: Pure transformer network for remote sensing image change detection","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5626814","DOI":"10.1109\/TGRS.2023.3336285","article-title":"GraSS: Contrastive Learning With Gradient-Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11157","DOI":"10.1109\/TNNLS.2023.3248871","article-title":"Augmentation-free graph contrastive learning of invariant-discriminative representations","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5610426","DOI":"10.1109\/TGRS.2023.3276853","article-title":"Self-supervised remote sensing feature learning: Learning paradigms, challenges, and future works","volume":"61","author":"Tao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"15055","DOI":"10.1109\/TITS.2021.3136287","article-title":"KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting","volume":"23","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ins.2021.12.077","article-title":"Curvature graph neural network","volume":"592","author":"Li","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_50","unstructured":"Peng, J., Ye, D., Tang, B., Lei, Y., Liu, Y., and Li, H. (2023). Lifelong learning with cycle memory networks. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.1109\/JSTARS.2023.3271312","article-title":"TOV: The original vision model for optical remote sensing image understanding via self-supervised learning","volume":"16","author":"Tao","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111637","DOI":"10.1016\/j.knosys.2024.111637","article-title":"LSTTN: A Long-Short Term Transformer-based spatiotemporal neural network for traffic flow forecasting","volume":"293","author":"Luo","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"128913","DOI":"10.1016\/j.physa.2023.128913","article-title":"STGC-GNNs: A GNN-based traffic prediction framework with a spatial\u2013temporal Granger causality graph","volume":"623","author":"He","year":"2023","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"120916","DOI":"10.1016\/j.ins.2024.120916","article-title":"CAT: A Causal Graph Attention Network for Trimming Heterophilic Graphs","volume":"677","author":"He","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"9774","DOI":"10.1109\/TPAMI.2023.3237896","article-title":"Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection","volume":"45","author":"Wu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.isprsjprs.2024.01.004","article-title":"ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning","volume":"208","author":"Dong","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning transferable visual models from natural language supervision. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_59","unstructured":"Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., and Duerig, T. (2021, January 18\u201324). Scaling up visual and vision-language representation learning with noisy text supervision. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_60","first-page":"1","article-title":"Three towers: Flexible contrastive learning with pretrained image models","volume":"36","author":"Kossen","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_61","unstructured":"Rosenfeld, E., Nakkiran, P., Pouransari, H., Tuzel, O., and Faghri, F. (2022). APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations. arXiv."},{"key":"ref_62","unstructured":"Li, J., Li, D., Savarese, S., and Hoi, S. (2023). Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wang, H., Vasu, P.K.A., Faghri, F., Vemulapalli, R., Farajtabar, M., Mehta, S., Rastegari, M., Tuzel, O., and Pouransari, H. (2023). Sam-clip: Merging vision foundation models towards semantic and spatial understanding. arXiv.","DOI":"10.1109\/CVPRW63382.2024.00367"},{"key":"ref_64","unstructured":"Li, Y., Wang, H., Duan, Y., and Li, X. (2023). Clip surgery for better explainability with enhancement in open-vocabulary tasks. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1007\/978-3-031-19815-1_40","article-title":"Extract Free Dense Labels from CLIP","volume":"Volume 13688","author":"Avidan","year":"2022","journal-title":"Computer Vision \u2013 ECCV 2022"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023). Segment anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_67","unstructured":"Zhang, C., Liu, L., Cui, Y., Huang, G., Lin, W., Yang, Y., and Hu, Y. (2023). A Comprehensive Survey on Segment Anything Model for Vision and Beyond. arXiv."},{"key":"ref_68","unstructured":"Tang, L., Xiao, H., and Li, B. (2023). Can sam segment anything? when sam meets camouflaged object detection. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Shi, P., Qiu, J., Abaxi, S.M.D., Wei, H., Lo, F.P.W., and Yuan, W. (2023). Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation. Diagnostics, 13.","DOI":"10.3390\/diagnostics13111947"},{"key":"ref_70","first-page":"1","article-title":"Segment everything everywhere all at once","volume":"36","author":"Zou","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_71","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","unstructured":"Yang, K., Xia, G.S., Liu, Z., Du, B., Yang, W., Pelillo, M., and Zhang, L. (2020). Semantic change detection with asymmetric Siamese networks. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4410213","DOI":"10.1109\/TGRS.2022.3168331","article-title":"ICIF-Net: Intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_75","first-page":"4401015","article-title":"Change detection on remote sensing images using dual-branch multilevel intertemporal network","volume":"61","author":"Feng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"4701611","DOI":"10.1109\/TGRS.2024.3362914","article-title":"ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection","volume":"62","author":"Noman","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Noman, M., Fiaz, M., and Cholakkal, H. (2024). ChangeBind: A Hybrid Change Encoder for Remote Sensing Change Detection. arXiv.","DOI":"10.1109\/IGARSS53475.2024.10640559"},{"key":"ref_78","unstructured":"Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., and Metzler, D. (2022). Emergent abilities of large language models. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/4109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:27:37Z","timestamp":1760113657000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/4109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"references-count":78,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16214109"],"URL":"https:\/\/doi.org\/10.3390\/rs16214109","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,3]]}}}