{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:40:18Z","timestamp":1782405618254,"version":"3.54.5"},"reference-count":71,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771363"],"award-info":[{"award-number":["41771363"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010843","name":"Guangzhou Science, Technology and Innovation Commission","doi-asserted-by":"publisher","award":["201802030008"],"award-info":[{"award-number":["201802030008"]}],"id":[{"id":"10.13039\/501100010843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, building change detection has made remarkable progress through using deep learning. The core problems of this technique are the need for additional data (e.g., Lidar or semantic labels) and the difficulty in extracting sufficient features. In this paper, we propose an end-to-end network, called the pyramid feature-based attention-guided Siamese network (PGA-SiamNet), to solve these problems. The network is trained to capture possible changes using a convolutional neural network in a pyramid. It emphasizes the importance of correlation among the input feature pairs by introducing a global co-attention mechanism. Furthermore, we effectively improved the long-range dependencies of the features by utilizing various attention mechanisms and then aggregating the features of the low-level and co-attention level; this helps to obtain richer object information. Finally, we evaluated our method with a publicly available dataset (WHU) building dataset and a new dataset (EV-CD) building dataset. The experiments demonstrate that the proposed method is effective for building change detection and outperforms the existing state-of-the-art methods on high-resolution remote sensing orthoimages in various metrics.<\/jats:p>","DOI":"10.3390\/rs12030484","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T03:18:48Z","timestamp":1580872728000},"page":"484","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":230,"title":["PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1560-5577","authenticated-orcid":false,"given":"Huiwei","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7294-7496","authenticated-orcid":false,"given":"Jinming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinqi","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5976","DOI":"10.3390\/rs6075976","article-title":"Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries","volume":"6","author":"Aleksandrowicz","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","unstructured":"(2019, January 25). Earth Watching. Available online: https:\/\/earth.esa.int\/web\/earth-watching\/change-detection."},{"key":"ref_3","unstructured":"(2019, May 10). Onera Satellite Change Detection. Available online: http:\/\/dase.grss-ieee.org."},{"key":"ref_4","first-page":"197","article-title":"2D Building Change Detection from High Resolution Aerial Images and Correlation Digital Surface Models","volume":"36","author":"Champion","year":"2007","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.compenvurbsys.2007.10.001","article-title":"Classification of the wildland\u2013urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography","volume":"32","author":"Cleve","year":"2008","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/JSTARS.2013.2252423","article-title":"Building Change Detection from Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/01431160601075582","article-title":"Object-based change detection using correlation image analysis and image segmentation","volume":"29","author":"Im","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2009.10.002","article-title":"Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge","volume":"65","author":"Bouziani","year":"2010","journal-title":"ISPRS J. Photogramm."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis-Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2016.09.013","article-title":"3D change detection\u2013Approaches and applications","volume":"122","author":"Qin","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"149","DOI":"10.5194\/isprs-annals-III-7-149-2016","article-title":"Building Change Detection in Very High Resolution Satellite Stereo Image Time Series","volume":"III-7","author":"Tian","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1080\/01431161.2012.725483","article-title":"Change detection of buildings from satellite imagery and lidar data","volume":"34","author":"Malpica","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","first-page":"669","article-title":"Building Change Detection by Combining Lidar data and Ortho Image","volume":"XLI-B3","author":"Peng","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1111\/phor.12063","article-title":"State of the art in high density image matching","volume":"29","author":"Remondino","year":"2014","journal-title":"Photogramm. Rec."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TGRS.2013.2240692","article-title":"Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models","volume":"52","author":"Tian","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Yang, J., Price, B., and Cohen, S. (July, January 26). Object contour detection with a fully convolutional encoder-decoder network. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., and Li, C. (2017, January 21\u201326). Residual Attention Network for Image Classification. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_20","unstructured":"Dai, J., He, K., and Sun, J. (July, January 26). Instance-aware semantic segmentation via multi-task network cascades. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_21","unstructured":"Zhang, Z., Vosselman, G., Gerke, M., Tuia, D., and Yang, M.Y. (2018, January 18\u201322). Change Detection between Multimodal Remote Sensing Data Using Siamese CNN. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lim, K.S., Jin, D.K., and Kim, C.S. (2018, January 12\u201315). Change Detection in High Resolution Satellite Images Using an Ensemble of Convolutional Neural Networks. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA.","DOI":"10.23919\/APSIPA.2018.8659603"},{"key":"ref_24","unstructured":"Daudt, R.C., Saux, B.L., and Boulch, A. (2018, January 7\u201310). Fully Convolutional Siamese Networks for Change Detection. Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_25","first-page":"565","article-title":"Change Detection in Remote Sensing Images Using Conditional Adversarial Networks","volume":"XLII-2","author":"Lebedev","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","unstructured":"Khan, S.H., He, X., Bennamoun, M., Porikli, F., Sohel, F., and Togneri, R. (July, January 26). Weakly Supervised Change Detection in a Pair of Images. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Khan, S.H., He, X., Porikli, F., Bennamoun, M., Sohel, F., and Togneri, R. (2016, January 19\u201325). Learning deep structured network for weakly supervised change detection. Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/279"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Caye Daudt, R., Le Saux, B., Boulch, A., and Gousseau, Y. (2019, January 16\u201320). Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00187"},{"key":"ref_29","unstructured":"Jong, K.L.D., and Bosman, A.S. (2019, February 22). Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks. Available online: https:\/\/arxiv.org\/abs\/1812.05815?context=cs.NE."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6960","DOI":"10.1109\/TGRS.2019.2909781","article-title":"Transferred Deep Learning-Based Change Detection in Remote Sensing Images","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, H., Wu, C., Du, B., and Zhang, L. (2019, July 01). Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images. Available online: https:\/\/arxiv.org\/abs\/1906.11479.","DOI":"10.1109\/Multi-Temp.2019.8866947"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovi\u0107, R., and Sivic, J. (2017, January 21\u201326). End-to-end weakly-supervised semantic alignment. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00723"},{"key":"ref_33","unstructured":"Kanazawa, A., Jacobs, D.W., and Chandraker, M. (2019, September 18). WarpNet: Weakly Supervised Matching for Single-View Reconstruction. Available online: https:\/\/arxiv.org\/abs\/1604.05592."},{"key":"ref_34","unstructured":"Huang, S., Wang, Q., Zhang, S., Yan, S., and He, X. (November, January 27). Dynamic Context Correspondence Network for Semantic Alignment. Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_35","unstructured":"Wang, F., and Tax, D.M.J. (2019, October 18). Survey on the Attention Based RNN Model and Its Applications in Computer Vision. Available online: https:\/\/arxiv.org\/abs\/1601.06823."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (In European Conference on Computer Vision (ECCV)), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2018, January 18\u201322). Dual Attention Network for Scene Segmentation. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., and Jia, J. (2018, January 8\u201314). PSANet: Point-wise Spatial Attention Network for Scene Parsing. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"ref_39","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2019, April 18). Pyramid Attention Network for Semantic Segmentation. Available online: https:\/\/arxiv.org\/abs\/1805.10180."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, X., Wang, W., Ma, C., Shen, J., Shao, L., and Porikli, F.M. (2019, January 16\u201320). See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00374"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2017, January 21\u201326). Squeeze-and-Excitation Networks. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_42","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Vedaldi, A. (2018, January 3\u20138). Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. Proceedings of the Conference and Workshop on Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_43","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., and Hu, H. (November, January 27). GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond. Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., and Agrawal, A. (2018, January 18\u201322). Context Encoding for Semantic Segmentation. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00747"},{"key":"ref_45","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.-S. (July, January 26). SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_46","unstructured":"Xiong, C., Zhong, V., and Socher, R. (2016, January 24\u201326). Dynamic Coattention Networks for Question Answering. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yu, Z., Yu, J., Cui, Y., Tao, D., and Tian, Q. (2019, January 16\u201320). Deep Modular Co-Attention Networks for Visual Question Answering. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00644"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Nguyen, D.-K., and Okatani, T. (2018, January 18\u201322). Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00637"},{"key":"ref_49","unstructured":"Lu, J., Yang, J., Batra, D., and Parikh, D. (2016, January 5\u201310). Hierarchical Question-Image Co-Attention for Visual Question Answering. Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS), Barcelona, Spain."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/TGRS.2016.2627638","article-title":"Cosegmentation for Object-Based Building Change Detection from High-Resolution Remotely Sensed Images","volume":"55","author":"Xiao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rahman, F., Vasu, B., Cor, J.V., Kerekes, J., and Savakis, A. (2018, January 26\u201329). Siamese Network with Multi-Level Features for Patch-Based Change Detection in Satellite Imagery. Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA.","DOI":"10.1109\/GlobalSIP.2018.8646512"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Saux, B.L., Boulch, A., and Gousseau, Y. (2018, January 22\u201327). Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_54","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of oriented gradients for human detection. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_56","unstructured":"Choy, C.B., Gwak, J., Savarese, S., and Chandraker, M. (2016, January 5\u201310). Universal Correspondence Network. Proceedings of the Conference and Workshop on Neural Information Processing Systems (NeurIPS), Barcelona, Spain."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Moo Yi, K., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., and Fua, P. (2017, January 21\u201326). Learning to Find Good Correspondences. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00282"},{"key":"ref_58","unstructured":"Chen, Y.-C., Huang, P.-H., Yu, L.-Y., Huang, J.-B., Yang, M.-H., and Lin, Y.-Y. (2018, January 2\u20136). Deep Semantic Matching with Foreground Detection and Cycle-Consistency. Proceedings of the 14th Asian Conference on Computer Vision (ACCV), Perth, Australia."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovi\u0107, R., and Sivic, J. (2017, January 21\u201326). Convolutional neural network architecture for geometric matching. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.12"},{"key":"ref_60","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":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","unstructured":"Rocco, I., Cimpoi, M., Arandjelovi\u0107, R., Torii, A., Pajdla, T., and Sivic, J. (2018, January 3\u20135). Neighbourhood Consensus Networks. Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_62","unstructured":"Chen, Y.-C., Lin, Y.-Y., Yang, M.-H., and Huang, J.-B. (2019, September 15). Show, Match and Segment: Joint Learning of Semantic Matching and Object Co-Segmentation. Available online: https:\/\/arxiv.org\/abs\/1906.05857?context=cs.CV."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cao, Z.-G., Xiong, X., Xian, K., and Qi, X. (2019, January 22\u201325). Salient Object Detection via Deep Hierarchical Context Aggregation and Multi-Layer Supervision. Proceedings of the IEEE International Conference on Image Processing (ICIP) 2019, Taiwan, China.","DOI":"10.1109\/ICIP.2019.8803738"},{"key":"ref_64","unstructured":"Liu, Y., Qiu, Y., Zhang, L., Bian, J., Nie, G.-Y., and Cheng, M.-M. (2019, May 25). Salient Object Detection via High-to-Low Hierarchical Context Aggregation. Available online: https:\/\/arxiv.org\/abs\/1812.10956."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_66","unstructured":"Kingma, D.P., and Ba, J. (2014, January 14\u201316). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AL, Canada."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Varghese, A., Gubbi, J., Ramaswamy, A., and Balamuralidhar, P. (2018, January 8\u201314). ChangeNet: A Deep Learning Architecture for Visual Change Detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-11012-3_10"},{"key":"ref_68","unstructured":"Sakurada, K. (2019, June 25). Weakly Supervised Silhouette-based Semantic Change Detection. Available online: https:\/\/arxiv.org\/abs\/1811.11985v1."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, M., and Wanbing, G. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_70","unstructured":"Liu, Y., Pang, C., Zhan, Z., Zhang, X., and Yang, X. (2019, October 18). Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model. Available online: https:\/\/arxiv.org\/abs\/1909.07726?context=cs.CV."},{"key":"ref_71","unstructured":"He, K., Zhang, J., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/484\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:54:19Z","timestamp":1760172859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,3]]},"references-count":71,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030484"],"URL":"https:\/\/doi.org\/10.3390\/rs12030484","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,3]]}}}