{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:08:03Z","timestamp":1771063683012,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection (CD) is a crucial task in remote sensing (RS) to distinguish surface changes from bitemporal images. Recently, deep learning (DL) based methods have achieved remarkable success for CD. However, the existing methods lack robustness to various kinds of changes in RS images, which suffered from problems of feature misalignment and inefficient supervision. In this paper, a deeply supervised attentive high-resolution network (DSAHRNet) is proposed for remote sensing image change detection. First, we design a spatial-channel attention module to decode change information from bitemporal features. The attention module is able to model spatial-wise and channel-wise contexts. Second, to reduce feature misalignment, the extracted features are refined by stacked convolutional blocks in parallel. Finally, a novel deeply supervised module is introduced to generate more discriminative features. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed DSAHRNet outperforms other state-of-the-art methods, and achieves a great trade-off between performance and complexity.<\/jats:p>","DOI":"10.3390\/rs15010045","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:02:15Z","timestamp":1671764535000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Deeply Supervised Attentive High-Resolution Network for Change Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4223-7183","authenticated-orcid":false,"given":"Jinming","family":"Wu","sequence":"first","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Chunhui","family":"Xie","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China"}]},{"given":"Zuxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yongxin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review article digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1109\/TGRS.2014.2363548","article-title":"Building change detection in multitemporal very high resolution SAR images","volume":"53","author":"Marin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","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":"ref_4","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.ins.2022.04.006","article-title":"AF2GNN: Graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification","volume":"602","author":"Ding","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sublime, J., and Kalinicheva, E. (2019). Automatic post-disaster damage mapping using deep-learning techniques for change detection: Case study of the Tohoku tsunami. Remote Sens., 11.","DOI":"10.3390\/rs11091123"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yao, D., Zhi-li, Z., Xiao-feng, Z., Wei, C., Fang, H., Yao-ming, C., and Cai, W.W. (Def. Technol., 2022). Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification, Def. Technol., in press.","DOI":"10.1016\/j.dt.2022.02.007"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5504205","DOI":"10.1109\/LGRS.2021.3062944","article-title":"Graph sample and aggregate-attention network for hyperspectral image classification","volume":"19","author":"Ding","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112636","DOI":"10.1016\/j.rse.2021.112636","article-title":"Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters","volume":"265","author":"Zheng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mahdavi, S., Salehi, B., Huang, W., Amani, M., and Brisco, B. (2019). A PolSAR change detection index based on neighborhood information for flood mapping. Remote Sens., 11.","DOI":"10.3390\/rs11161854"},{"key":"ref_10","first-page":"1","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_11","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":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"3416","DOI":"10.1109\/TGRS.2009.2022633","article-title":"Change detection in optical aerial images by a multilayer conditional mixed Markov model","volume":"47","author":"Benedek","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., and Gousseau, Y. (2018, January 22\u201327). Urban change detection for multispectral earth observation using convolutional neural networks. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A general end-to-end 2D CNN framework for hyperspectral image change detection","volume":"57","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1080\/014311698216062","article-title":"Change vector analysis: A technique for the multispectral monitoring of land cover and condition","volume":"19","author":"Johnson","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.isprsjprs.2007.07.002","article-title":"Remote sensing research issues of the national land use change program of China","volume":"62","author":"Zhang","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1109\/TGRS.2013.2266673","article-title":"Slow feature analysis for change detection in multispectral imagery","volume":"52","author":"Wu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(97)00162-4","article-title":"Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: New approaches to change detection studies","volume":"64","author":"Nielsen","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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":"ref_21","doi-asserted-by":"crossref","unstructured":"Gapper, J.J., El-Askary, H., Linstead, E., and Piechota, T. (2019). Coral Reef change Detection in Remote Pacific islands using support vector machine classifiers. Remote Sens., 11.","DOI":"10.3390\/rs11131525"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"024514","DOI":"10.1117\/1.JRS.13.024514","article-title":"Building change detection from remotely sensed images based on spatial domain analysis and Markov random field","volume":"13","author":"Zong","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2005.09.008","article-title":"A change detection model based on neighborhood correlation image analysis and decision tree classification","volume":"99","author":"Im","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wessels, K.J., Van den Bergh, F., Roy, D.P., Salmon, B.P., Steenkamp, K.C., MacAlister, B., Swanepoel, D., and Jewitt, D. (2016). Rapid land cover map updates using change detection and robust random forest classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8110888"},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","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":"ref_29","unstructured":"Bandara, W.G.C., and Patel, V.M. (2022). Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images. arXiv."},{"key":"ref_30","unstructured":"Chen, Y., and Bruzzone, L. (2021). Self-supervised Remote Sensing Images Change Detection at Pixel-level. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ma, A., Zhang, L., and Zhong, Y. (2021, January 11\u201317). Change is everywhere: Single-temporal supervised object change detection in remote sensing imagery. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01491"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Caye Daudt, R., Le Saux, B., Boulch, A., and Gousseau, Y. (2019, January 16\u201317). Guided anisotropic diffusion and iterative learning for weakly supervised change detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00187"},{"key":"ref_33","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":"ref_34","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2022.02.021","article-title":"FCCDN: Feature constraint network for VHR image change detection","volume":"187","author":"Chen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","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":"ref_36","doi-asserted-by":"crossref","unstructured":"Bandara, W.G.C., and Patel, V.M. (2022). A transformer-based siamese network for change detection. arXiv.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"ref_37","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":"ref_38","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., and Zhang, M. (2020). PGA-SiamNet: Pyramid feature-based attention-guided Siamese network for remote sensing orthoimagery building change detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Wang, M., Tan, K., Jia, X., Wang, X., and Chen, Y. (2020). A deep siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12020205"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Xu, J., Luo, C., Chen, X., Wei, S., and Luo, Y. (2021). Remote sensing change detection based on multidirectional adaptive feature fusion and perceptual similarity. Remote Sens., 13.","DOI":"10.3390\/rs13153053"},{"key":"ref_43","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), Athens, Greece."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1109\/JSTARS.2020.2974276","article-title":"Deep depthwise separable convolutional network for change detection in optical aerial images","volume":"13","author":"Liu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R., Bradley, A., Papa, J.P., and Belagiannis, V. (2018). Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings, Springer.","DOI":"10.1007\/978-3-030-00889-5"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fu, L., Li, Y., and Zhang, Y. (2021). Hdfnet: Hierarchical dynamic fusion network for change detection in optical aerial images. Remote Sens., 13.","DOI":"10.3390\/rs13081440"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7296","DOI":"10.1109\/TGRS.2020.3033009","article-title":"Optical remote sensing image change detection based on attention mechanism and image difference","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Diakogiannis, F.I., Waldner, F., and Caccetta, P. (2021). Looking for change? Roll the dice and demand attention. Remote Sens., 13.","DOI":"10.3390\/rs13183707"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X. (2017, January 21\u201326). Residual attention network for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_54","unstructured":"Guo, M.H., Lu, C.Z., Liu, Z.N., Cheng, M.M., and Hu, S.M. (2022). Visual attention network. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_56","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_57","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Fort Lauderdale, FL, USA."},{"key":"ref_58","unstructured":"Hadsell, R., Chopra, S., and LeCun, Y. (2006, January 17\u201322). Dimensionality reduction by learning an invariant mapping. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lebedev, M., Vizilter, Y.V., Vygolov, O., Knyaz, V., and Rubis, A.Y. (2018, January 4\u20137). Change detection in remote sensing images using conditional adversarial networks. Proceedings of the International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Riva del Garda, Italy.","DOI":"10.5194\/isprs-archives-XLII-2-565-2018"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:01Z","timestamp":1760147281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,22]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010045"],"URL":"https:\/\/doi.org\/10.3390\/rs15010045","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,22]]}}}