{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:26:41Z","timestamp":1772832401438,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"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":["62071336"],"award-info":[{"award-number":["62071336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar (SAR) imagery change detection (CD) is still a crucial and challenging task. Recently, with the boom of deep learning technologies, many deep learning methods have been presented for SAR CD, and they achieve superior performance to traditional methods. However, most of the available convolutional neural networks (CNN) approaches use diminutive and single convolution kernel, which has a small receptive field and cannot make full use of the context information and some useful detail information of SAR images. In order to address the above drawback, pyramidal convolutional block attention network (PCBA-Net) is proposed for SAR image CD in this study. The proposed PCBA-Net consists of pyramidal convolution (PyConv) and convolutional block attention module (CBAM). PyConv can not only extend the receptive field of input to capture enough context information, but also handles input with incremental kernel sizes in parallel to obtain multi-scale detailed information. Additionally, CBAM is introduced in the PCBA-Net to emphasize crucial information. To verify the performance of our proposed method, six actual SAR datasets are used in the experiments. The results of six real SAR datasets reveal that the performance of our approach outperforms several state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs14225762","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:36:36Z","timestamp":1668566196000},"page":"5762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["PCBA-Net: Pyramidal Convolutional Block Attention Network for Synthetic Aperture Radar Image Change Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Yufa","family":"Xia","sequence":"first","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430079, China"}]},{"given":"Xin","family":"Xu","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1490-0347","authenticated-orcid":false,"given":"Fangling","family":"Pu","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TIP.2004.838698","article-title":"Image change detection algorithms: A systematic survey","volume":"14","author":"Radke","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.isprsjprs.2017.05.001","article-title":"Feature learning and change feature classification based on deep learning for ternary change detection in SAR images","volume":"129","author":"Gong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, W., Shi, J., Fei, T., and Zong, B. (2022). Joint detection threshold optimization and illumination time allocation strategy for cognitive tracking in a networked radar system. IEEE Trans. Signal Process.","DOI":"10.1109\/TSP.2022.3188205"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4449","DOI":"10.3390\/rs14184449","article-title":"Unsupervised Radar Target Detection under Complex Clutter Background Based on Mixture Variational Autoencoder","volume":"14","author":"Liang","year":"2022","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","article-title":"Earthquake damage assessment of buildings using VHR optical and SAR imagery","volume":"48","author":"Brunner","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4288","DOI":"10.1109\/JSTARS.2014.2347171","article-title":"Improving SAR-based urban change detection by combining MAP-MRF classifier and nonlocal means similarity weights","volume":"7","author":"Yousif","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.rse.2013.08.050","article-title":"Change detection of boreal forest using bi-temporal ALOS PALSAR backscatter data","volume":"155","author":"Pantze","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4712","DOI":"10.1109\/TGRS.2015.2407953","article-title":"Change-detection map learning using matching pursuit","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.neucom.2014.06.024","article-title":"Semi-supervised change detection method for multi-temporal hyperspectral images","volume":"148","author":"Yuan","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TGRS.2009.2012407","article-title":"Unsupervised change detection from multichannel SAR data by Markovian data fusion","volume":"47","author":"Moser","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/TIP.2002.999678","article-title":"An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images","volume":"11","author":"Bruzzone","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TGRS.2019.2901945","article-title":"A deep learning method for change detection in synthetic aperture radar images","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"1","article-title":"Change detection in synthetic aperture radar images using a dual-domain network","volume":"19","author":"Qu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1109\/TGRS.2006.876288","article-title":"Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery","volume":"44","author":"Moser","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/LGRS.2006.869973","article-title":"Automatic identification of the number and values of decision thresholds in the log-ratio image for change detection in SAR images","volume":"3","author":"Bazi","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/TGRS.2007.893568","article-title":"A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis","volume":"45","author":"Inglada","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.1109\/JSTARS.2014.2328344","article-title":"Unsupervised change detection in SAR image based on Gauss-log ratio image fusion and compressed projection","volume":"7","author":"Hou","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1109\/LGRS.2012.2191387","article-title":"Wavelet fusion on ratio images for change detection in SAR images","volume":"9","author":"Ma","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.patcog.2016.07.040","article-title":"Unsupervised saliency-guided SAR image change detection","volume":"61","author":"Zheng","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040221","article-title":"Change detection in SAR images via ratio-based gaussian kernel and nonlocal theory","volume":"60","author":"Zhuang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"1","article-title":"Structure consistency-based graph for unsupervised change detection with homogeneous and heterogeneous remote sensing images","volume":"60","author":"Sun","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0031-3203(86)90030-0","article-title":"Minimum error thresholding","volume":"19","author":"Kittler","year":"1986","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1080\/01431161.2013.871596","article-title":"Unsupervised change detection in SAR images based on locally fitting model and semi-EM algorithm","volume":"35","author":"Su","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and $ k $-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TIP.2011.2170702","article-title":"Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering","volume":"21","author":"Gong","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TIP.2010.2040763","article-title":"A robust fuzzy local information C-means clustering algorithm","volume":"19","author":"Krinidis","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.ins.2018.08.015","article-title":"A novel edge-weight based fuzzy clustering method for change detection in SAR images","volume":"467","author":"Tian","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/LGRS.2016.2611001","article-title":"Automatic change detection in synthetic aperture radar images based on PCANet","volume":"13","author":"Gao","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/LGRS.2019.2895656","article-title":"Sea ice change detection in SAR images based on convolutional-wavelet neural networks","volume":"16","author":"Gao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10748","DOI":"10.1109\/JSTARS.2021.3120381","article-title":"Synthetic Aperture Radar Image Change Detection via Siamese Adaptive Fusion Network","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TNNLS.2018.2847309","article-title":"Local restricted convolutional neural network for change detection in polarimetric SAR images","volume":"30","author":"Liu","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102726","DOI":"10.1016\/j.ipm.2021.102726","article-title":"Semisupervised SAR image change detection based on a siamese variational autoencoder","volume":"59","author":"Zhao","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_35","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2014). Object detectors emerge in deep scene cnns. arXiv."},{"key":"ref_36","unstructured":"Duta, I.C., Liu, L., Zhu, F., and Shao, L. (2020). Pyramidal convolution: Rethinking convolutional neural networks for visual recognition. arXiv."},{"key":"ref_37","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1109\/TIP.2002.804276","article-title":"Speckle reducing anisotropic diffusion","volume":"11","author":"Yu","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","first-page":"223","article-title":"A coefficient of agreement as a measure of thematic classification accuracy","volume":"52","author":"Rosenfield","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5762\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:18:32Z","timestamp":1760145512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,15]]},"references-count":40,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225762"],"URL":"https:\/\/doi.org\/10.3390\/rs14225762","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,15]]}}}