{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T20:27:16Z","timestamp":1769200036827,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T00:00:00Z","timestamp":1643932800000},"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":["61671354"],"award-info":[{"award-number":["61671354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["there is no grant number"],"award-info":[{"award-number":["there is no grant number"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Innovation Team Project","award":["there is no grant number"],"award-info":[{"award-number":["there is no grant number"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar (SAR) image change detection (CD) aims to automatically recognize changes over the same geographic region by comparing prechange and postchange SAR images. However, the detection performance is usually subject to several restrictions and problems, including the absence of labeled SAR samples, inherent multiplicative speckle noise, and class imbalance. More importantly, for bitemporal SAR images, changed regions tend to present highly variable sizes, irregular shapes, and different textures, typically referred to as hybrid variabilities, further bringing great difficulties to CD. In this paper, we argue that these internal hybrid variabilities can also be used for learning stronger feature representation, and we propose a hybrid variability aware network (HVANet) for completely unsupervised label-free SAR image CD by taking inspiration from recent developments in deep self-supervised learning. First, since different changed regions may exhibit hybrid variabilities, it is necessary to enrich distinguishable information within the input features. To this end, in shallow feature extraction, we generalize the traditional spatial patch (SP) feature to allow for each pixel in bitemporal images to be represented at diverse scales and resolutions, called extended SP (ESP). Second, with the carefully customized ESP features, HVANet performs local spatial structure information extraction and multiscale\u2013multiresolution (MS-MR) information encoding simultaneously through a local spatial stream and a scale-resolution stream, respectively. Intrinsically, HVANet projects the ESP features into a new high-level feature space, where the change identification becomes easier. Third, to train the framework effectively, a self-supervision layer is attached to the top of the HVANet to enable the two-stream feature learning and recognition of changed pixels in the corresponding feature space, in a self-supervised manner. Experimental results on three low\/medium-resolution SAR datasets demonstrate the effectiveness and superiority of the proposed framework in unsupervised SAR CD tasks.<\/jats:p>","DOI":"10.3390\/rs14030734","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:38:40Z","timestamp":1644179920000},"page":"734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hybrid Variability Aware Network (HVANet): A Self-Supervised Deep Framework for Label-Free SAR Image Change Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9729-6052","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7993-2809","authenticated-orcid":false,"given":"Yinghua","family":"Wang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,4]]},"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","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2015.2443494","article-title":"The time variable in data fusion: A change detection perspective","volume":"3","author":"Bovolo","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1109\/TGRS.2005.857987","article-title":"A detail-preserving scale-driven approach to change detection in multitemporal SAR images","volume":"43","author":"Bovolo","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2463","DOI":"10.1080\/01431160119991","article-title":"Land surface change detection in a desert area in Algeria using multitemporal ERS SAR coherence images","volume":"22","author":"Liu","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1109\/36.602528","article-title":"An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images","volume":"35","author":"Bruzzone","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"858","article-title":"Earthquake damage assessment of buildings using VHR optical and SAR imagery","volume":"48","author":"Burnner","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2019.2909143","article-title":"Unbiased seamless SAR image change detection based on normalized compression distance","volume":"12","author":"Coca","year":"2019","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/JSTARS.2019.2960518","article-title":"SAR image change detection based on nonlocal low-rank model and two-level clustering","volume":"13","author":"Sun","year":"2020","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_10","unstructured":"Oliver, C., and Quegan, S. (1998). Understanding Synthetic Aperture Radar Images, Artech House."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1109\/TGRS.2020.3000296","article-title":"Building change detection in VHR SAR images via unsupervised deep transcoding","volume":"59","author":"Saha","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1109\/LGRS.2018.2878420","article-title":"Imbalanced learning-based automatic SAR images change detection by morphologically supervised PCA-Net","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TGRS.2005.861007","article-title":"Unsupervised change detection on SAR images using fuzzy hidden Markov chains","volume":"44","author":"Carincotte","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1109\/TGRS.2013.2238946","article-title":"Nonparametric change detection in multitemporal SAR images based on mean-shift clustering","volume":"51","author":"Aiazzi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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 multitemporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1109\/LGRS.2015.2484220","article-title":"Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4605","DOI":"10.1109\/TGRS.2018.2829630","article-title":"Deformable dictionary learning for SAR image change detection","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"4700221","article-title":"Structure consistency-based graph for unsupervised change detection with homogeneous and heterogeneous remote sensing images","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/36.239913","article-title":"Change detection techniques for ERS-1 SAR data","volume":"31","author":"Rignot","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/LGRS.2009.2026188","article-title":"Multiscale change detection in multitemporal satellite images","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1109\/TGRS.2010.2066979","article-title":"Multitemporal image change detection using undecimated discrete wavelet transform and active contours","volume":"49","author":"Celik","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1170","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_26","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/LGRS.2011.2166149","article-title":"A threshold selection method using two SAR change detection measures based on the Markov random field model","volume":"9","author":"Xiong","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1109\/LGRS.2015.2418575","article-title":"Unstructured versus structured GLRT for multipolarization SAR change detection","volume":"12","author":"Carotenuto","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/TGRS.2009.2029095","article-title":"Unsupervised change detection for satellite images using dual-tree complex wavelet transform","volume":"48","author":"Celik","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"2963","article-title":"Saliency-guided deep neural networks for SAR image change detection","volume":"43","author":"Geng","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4701","DOI":"10.1109\/JSTARS.2018.2866540","article-title":"SAR image change detection using saliency extraction and Shearlet transform","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/LGRS.2018.2876616","article-title":"An, L. SAR image change detection using PCANet guided by saliency detection","volume":"16","author":"Li","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12174","DOI":"10.1109\/JSTARS.2021.3126839","article-title":"Change detection in SAR images based on improved non-subsampled Shearlet transform and multi-scale feature fusion CNN","volume":"14","author":"Shen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/LGRS.2020.2977838","article-title":"SAR image change detection based on multiscale capsule network","volume":"18","author":"Gao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/LGRS.2019.2906279","article-title":"Transferred deep learning for sea ice change detection from synthetic-aperture radar images","volume":"16","author":"Gao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ajadi, O.A., Meyer, F.J., and Webley, P.W. (2016). Change detection in synthetic aperture radar images using a multiscale-driven approach. Remote Sens., 8.","DOI":"10.3390\/rs8060482"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TNNLS.2015.2435783","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. Learn. Syst."},{"key":"ref_38","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_39","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_40","doi-asserted-by":"crossref","first-page":"4013405","DOI":"10.1109\/LGRS.2021.3073900","article-title":"Change detection in synthetic aperture radar images using a dual-domain network","volume":"19","author":"Qu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tan, Y., Li, J., Zhang, P., Gou, S., Wang, P., Chen, Y., Chen, J.-W., and Sun, C. (August, January 28). Bitemporal fully polarimetric SAR images change detection via nearest regularized joint sparse and transfer dictionary learning. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8897906"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hong, D., Guo, L., Yao, J., Yokoya, A., Chanussot, J., Heiden, U., and Zhang, B. (2021). Endmember-guided unmixing network (EGU-Net): A general deep learning framework for self-supervised hyperspectral unmixing. arXiv.","DOI":"10.1109\/TNNLS.2021.3082289"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5501813","DOI":"10.1109\/TGRS.2021.3057768","article-title":"Self-Supervised learning with adaptive distillation for hyperspectral image classification","volume":"60","author":"Yue","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/JSTARS.2020.3036602","article-title":"Self-supervised pretraining of transformers for satellite image time series classification","volume":"14","author":"Yuan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, X., He, K., and Gupta, A. (2017, January 22\u201329). Transitive invariance for self-supervised visual representation learning. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.149"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1109\/JSTARS.2020.3047677","article-title":"Urban flood mapping with bitemporal multispectral imagery via a self-supervised learning framework","volume":"14","author":"Peng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_47","first-page":"5402812","article-title":"Self-supervised change detection in multiview remote sensing images","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4405710","DOI":"10.1109\/TGRS.2021.3109957","article-title":"Self-supervised multisensor change detection","volume":"60","author":"Saha","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1109\/TIP.2021.3128330","article-title":"Seeing like a human: Asynchronous learning with dynamic progressive refinement for person re-identification","volume":"31","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5724","DOI":"10.1073\/pnas.1524160113","article-title":"Perceptual learning modifies the functional specializations of visual cortical areas","volume":"113","author":"Chen","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2011.272","article-title":"Context-aware saliency detection","volume":"10","author":"Goferman","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1109\/TSP.2005.843707","article-title":"Complete-to-overcomplete discrete wavelet transforms: Theory and applications","volume":"53","author":"Andreopoulos","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1109\/TGRS.2015.2503045","article-title":"Spatial methods for multispectral pansharpening: Multiresolution analysis demystified","volume":"54","author":"Alparone","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"11903","DOI":"10.1109\/JSTARS.2021.3122461","article-title":"LCS-EnsemNet: A semisupervised deep neural network for SAR image change detection with dual feature extraction and label-consistent self-ensemble","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_55","unstructured":"Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20\u201325). Learning a similarity metric discriminatively, with application to face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2015). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2006, January 4\u20139). Greedy layer-wise training of deep networks. Proceedings of the Neural Information Processing Systems, Vancouver, BC, Canada.","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lee, H., Grosse, R., Ranganath, R., and Ng, A.Y. (2009, January 14\u201318). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the International Conference on Machine Learning, Montreal, BC, Canada.","DOI":"10.1145\/1553374.1553453"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_62","unstructured":"Xie, J., Girshick, R., and Farhadi, A. (2016, January 19\u201324). Unsupervised deep embedding for clustering analysis. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_63","unstructured":"(2021, December 16). Keras. Available online: https:\/\/keras.io\/."},{"key":"ref_64","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_65","unstructured":"Jolliffe, I. (2002). Principal Component Analysis, Springer."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1109\/TIP.2002.999679","article-title":"Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition","volume":"11","author":"Liu","year":"2002","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/734\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:14:08Z","timestamp":1760134448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,4]]},"references-count":66,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030734"],"URL":"https:\/\/doi.org\/10.3390\/rs14030734","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,4]]}}}