{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:49:09Z","timestamp":1760233749591,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T00:00:00Z","timestamp":1613520000000},"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":["61701154"],"award-info":[{"award-number":["61701154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Anhui Provience","award":["1808085QF185"],"award-info":[{"award-number":["1808085QF185"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M630703"],"award-info":[{"award-number":["2018M630703"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["NO.PA2020GDSK0080"],"award-info":[{"award-number":["NO.PA2020GDSK0080"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Complementary information between two difference images (DI\u2019s) has great contribution to improve change detection performances. Based on the effectiveness and flexibility of the multiple kernel learning (MKL) in information fusion, we develop a multiple kernel graph cut (MKGC) algorithm for synthetic aperture radar (SAR) image change detection. An energy function containing a weighted summation kernel is proposed for fusing the complementary information between the subtraction image and the ratio image. By iteratively minimizing the energy function, the kernel weights, region parameters and region labels are estimated automatically and optimally. Besides of it avoids modeling, MKGC also has a complete description of the changed areas and the strong noise immunity. Experiments on real GaoFen-3 SAR data set demonstrate the effectiveness of the MKGC algorithm, and illustrate that it is a good candidate for SAR image change detection.<\/jats:p>","DOI":"10.3390\/rs13040725","type":"journal-article","created":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T01:47:33Z","timestamp":1613526453000},"page":"725","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multiple Kernel Graph Cut for SAR Image Change Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7982-797X","authenticated-orcid":false,"given":"Lu","family":"Jia","sequence":"first","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"},{"name":"Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiantian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"},{"name":"Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feibiao","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Digital Change Detection Techniques Using Remotely Sensed Data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. 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Proceedings of the FIG Congress, Istanbul, Turkey.","key":"ref_4","DOI":"10.1117\/1.JRS.11.042620"},{"doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, R., Ding, F., Liu, B., Jiao, L., and Zhang, J. (2020). A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images. Remote Sens., 12.","key":"ref_5","DOI":"10.3390\/rs12101619"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2013.2275738","article-title":"Using Combined Difference Image and k -Means Clustering for SAR Image Change Detection","volume":"11","author":"Zheng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/JSTARS.2012.2200879","article-title":"Fusion of Difference Images for Change Detection Over Urban Areas","volume":"5","author":"Du","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_8","first-page":"3405","article-title":"Remote-Sensing Image Change Detection With Fusion of Multiple Wavelet Kernels","volume":"9","author":"Jia","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"43970","DOI":"10.1109\/ACCESS.2019.2908282","article-title":"SAR Image Change Detection Based on Mathematical Morphology and the K-Means Clustering Algorithm","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11426","DOI":"10.1109\/ACCESS.2019.2962622","article-title":"Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2011.2171493","article-title":"A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images","volume":"50","author":"Bovolo","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","first-page":"512","article-title":"Unsupervised Change Detection of Flood affected areas in SAR images using Rayleigh based Bayesian Thresholding","volume":"12","author":"Sumaiya","year":"2018","journal-title":"IET Radar Sonar Nav."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2017.03.020","article-title":"A Markov Random Field Integrating Spectral Dissimilarity and Class Co-occurrence Dependency for Remote Sensing Image Classification Optimization","volume":"128","author":"Wang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Shang, Y., Cao, G., and Zhang, Y. (2018, January 22\u201327). Change Detection Based on Fully-Connected Conditional Random Field with Region Potential in Remote Sensing Images. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","key":"ref_14","DOI":"10.1109\/IGARSS.2018.8518555"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TGRS.2008.916201","article-title":"Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection","volume":"46","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/LGRS.2012.2189092","article-title":"Unsupervised Change Detection With Kernels","volume":"9","author":"Volpi","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","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 Classification Change Detection in SAR Images","volume":"129","author":"Gong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7365","DOI":"10.1109\/TGRS.2019.2913095","article-title":"Saliency-Guided Deep Neural Networks for SAR Image Change Detection","volume":"57","author":"Geng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1109\/TGRS.2015.2493730","article-title":"Change Detection between SAR Images Using a Pointwise Approach and Graph theory","volume":"54","author":"Pham","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TIP.2010.2066982","article-title":"Multiregion Image Segmentation by Parametric Kernel Graph Cuts","volume":"20","author":"Salah","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/TFUZZ.2011.2170175","article-title":"Multiple Kernel Fuzzy clustering","volume":"20","author":"Huang","year":"2004","journal-title":"IEEE. Trans. Fuzzy Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast Approximate Energy Minimization via Graph Cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. 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