{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:52:48Z","timestamp":1784051568800,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,5,4]],"date-time":"2017-05-04T00:00:00Z","timestamp":1493856000000},"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":["41606198"],"award-info":[{"award-number":["41606198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41576011"],"award-info":[{"award-number":["41576011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2015M582140"],"award-info":[{"award-number":["2015M582140"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Province Natural Science Foundation of China","award":["ZR2016FB02"],"award-info":[{"award-number":["ZR2016FB02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of Earth observation programs, more and more multi-temporal synthetic aperture radar (SAR) data are available from remote sensing platforms. Therefore, it is demanding to develop unsupervised methods for SAR image change detection. Recently, deep learning-based methods have displayed promising performance for remote sensing image analysis. However, these methods can only provide excellent performance when the number of training samples is sufficiently large. In this paper, a novel simple method for SAR image change detection is proposed. The proposed method uses two singular value decomposition (SVD) analyses to learn the non-linear relations between multi-temporal images. By this means, the proposed method can generate more representative feature expressions with fewer samples. Therefore, it provides a simple yet effective way to be designed and trained easily. Firstly, deep semi-nonnegative matrix factorization (Deep Semi-NMF) is utilized to select pixels that have a high probability of being changed or unchanged as samples. Next, image patches centered at these sample pixels are generated from the input multi-temporal SAR images. Then, we build SVD networks, which are comprised of two SVD convolutional layers and one histogram feature generation layer. Finally, pixels in both multi-temporal SAR images are classified by the SVD networks, and then the final change map can be obtained. The experimental results of three SAR datasets have demonstrated the effectiveness and robustness of the proposed method.<\/jats:p>","DOI":"10.3390\/rs9050435","type":"journal-article","created":{"date-parts":[[2017,5,4]],"date-time":"2017-05-04T11:44:18Z","timestamp":1493898258000},"page":"435","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1825-328X","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqiang","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muwei","family":"Jian","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shao, P., Shi, W., He, P., Hao, M., and Zhang, X. (2016). Novel approach to unsupervised change detection based on a robust semi-supervised FCM. Remote Sens., 8.","DOI":"10.3390\/rs8030264"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Navarro, A., Rolim, J., Miguel, I., Catal\u00e3o, J., Silva, J., Painho, M., and Vekerdy, Z. (2016). Crop monitoring based on SPOT-5 take-5 and Sentinel-1A data for the estimation of crop water requirements. Remote Sens., 8.","DOI":"10.3390\/rs8060525"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.3390\/rs3102148","article-title":"Urban sprawl analysis and modeling in Asmara, Eritrea","volume":"3","author":"Tewolde","year":"2011","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"083639","DOI":"10.1117\/1.JRS.8.083639","article-title":"Using multi-temporal Landsat imagery to monitor and model the influence of landscape pattern on urban expansion in a metropolitan region","volume":"8","author":"Yang","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.3390\/rs5031024","article-title":"River Courses Affected by landslides and implications for hazard assessment: A high resolution remote sensing case study in NE Iraq\u2013W Iran","volume":"5","author":"Othman","year":"2013","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"084684","DOI":"10.1117\/1.JRS.8.084684","article-title":"Snow cover area identification by using a change detection method applied to COSMO-SkyMed images","volume":"8","author":"Pettinato","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_7","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. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"3248","DOI":"10.1109\/JSTARS.2014.2344017","article-title":"Unsupervised change detection in multi-temporal SAR images over large urban areas","volume":"7","author":"Hu","year":"2014","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1109\/LGRS.2016.2554606","article-title":"Unsupervised SAR image change detection based on SIFT keypoints and region information","volume":"13","author":"Wang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2073","DOI":"10.1109\/TGRS.2004.835304","article-title":"Application of log-cumulants to the detection of spatiotemporal discontinuities in multi-temporal SAR images","volume":"42","author":"Bujor","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/TGRS.2007.893568","article-title":"A new statistical similarity measure for change detection in multi-temporal SAR images and its extension to multiscale change analysis","volume":"45","author":"Inglada","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/36.843009","article-title":"Automatic analysis of the difference image for unsupervised change detection","volume":"38","author":"Bruzzone","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TGRS.2004.842441","article-title":"An unsupervised approach based on the generalized Gaussian model to automatic change detection in multi-temporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1016\/j.sigpro.2009.10.018","article-title":"A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images","volume":"90","author":"Celik","year":"2010","journal-title":"Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.isprsjprs.2014.04.010","article-title":"SAR change detection based on intensity and texture changes","volume":"93","author":"Gong","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","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":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","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-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1109\/LGRS.2015.2484220","article-title":"Gabor feature based unsupervised change detection of multi-temporal SAR images based on two-level clustering","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1109\/LGRS.2016.2619163","article-title":"Change detection based on multifeature probabilistic ensemble conditional random field model for high spatial resolution remote sensing imagery","volume":"13","author":"Lv","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4645","DOI":"10.1007\/s00500-014-1460-0","article-title":"Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images","volume":"20","author":"Liu","year":"2014","journal-title":"Soft Comput."},{"key":"ref_22","first-page":"125","article-title":"Change detection in synthetic aperture radar images based on deep neural networks","volume":"27","author":"Gong","year":"2016","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_23","first-page":"1","article-title":"A deep convolutional coupling network for change detection based on heterogeneous optical and radar images","volume":"28","author":"Liu","year":"2017","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.02.013","article-title":"Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images","volume":"116","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/LGRS.2014.2349937","article-title":"Change detection based on pulse-coupled neural networks and the NMF feature for high spatial resolution remote sensing imagery","volume":"12","author":"Zhong","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1016\/j.patcog.2014.12.016","article-title":"Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding","volume":"48","author":"Zhang","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1109\/JSTARS.2017.2655516","article-title":"R-VCANet: A new deep learning-based hyperspectral image classification method","volume":"5","author":"Pan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5832","DOI":"10.1109\/TGRS.2016.2572736","article-title":"Ship detection in spaceborne optical image with SVD networks","volume":"54","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, R., Du, B., and Zhang, L. (2016). Hyperspectral unmixing via double abundance characteristics constraints based NMF. Remote Sens., 8.","DOI":"10.3390\/rs8060464"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TSP.2014.2306181","article-title":"Linear-quadratic blind source separation using NMF to unmix urban hyperspectral images","volume":"7","author":"Meganem","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, S., Elhoseiny, M., Elgammal, A., and Yang, D. (2014, January 27\u201330). Improving non-negative matrix factorization via ranking its bases. Proceedings of the 2014 IEEE International Conference on Image Processing, Paris, France.","DOI":"10.1109\/ICIP.2014.7026201"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1016\/S0167-8655(03)00089-8","article-title":"Introducing a weighted non-negative matrix factorization for image classification","volume":"14","author":"Guillanmet","year":"2003","journal-title":"Pattern Recognit. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TPAMI.2008.277","article-title":"Convex and semi-nonnegative matrix factorization","volume":"32","author":"Ding","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"Trigeorgis, G., Bousamlis, K., Zafeiriou, S., and Schuller, B. (2014, January 21\u201326). A deep Semi-NMF model for learning hidden representations. Proceedings of the 31st International Conference on Machine Learning, Beijing, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hsu, C.-C., Chien, J.-T., and Chi, T.-S. (2015, January 6\u201310). Layered nonnegative matrix factorization for speech separation. Proceedings of the Interspeech 2015, Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-217"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"046019","DOI":"10.1117\/1.JRS.10.046019","article-title":"Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine","volume":"10","author":"Gao","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TFUZZ.2013.2249072","article-title":"Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images","volume":"22","author":"Gong","year":"2014","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_40","first-page":"1871","article-title":"LIBLINEAR: A library for large linear classification","volume":"9","author":"Fan","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/5\/435\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:34:38Z","timestamp":1760207678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/5\/435"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,4]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2017,5]]}},"alternative-id":["rs9050435"],"URL":"https:\/\/doi.org\/10.3390\/rs9050435","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,4]]}}}