{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:08:31Z","timestamp":1760148511289,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Major Program of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42192582","2022YFB3902000"],"award-info":[{"award-number":["42192582","2022YFB3902000"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42192582","2022YFB3902000"],"award-info":[{"award-number":["42192582","2022YFB3902000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by reconstructing the background. This study proposed a novel spatial\u2013spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering. We used the two-branch 3D convolutional autoencoder to fully extract the spatial\u2013spectral joint features and spectral interband features of HSI. In addition, we used a morphological filter and a total variance curvature filter for spatial detection. Currently, most of the datasets used to validate the performance of HAD methods are airborne HSI, and there are few available satellite-borne HSI. For this reason, we constructed a dataset of satellite-borne HSI based on the GF-5 satellite for experimental validation of our anomaly detection method. The experimental results for the airborne and satellite-borne HSI demonstrated the superior performance of the proposed method compared with six state-of-the-art methods. The area under the curve (AUC) values of our proposed method on different HSI reached above 0.9, which is higher than those of the other methods.<\/jats:p>","DOI":"10.3390\/rs15102542","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T10:49:51Z","timestamp":1683888591000},"page":"2542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Spatial\u2013Spectral Joint Hyperspectral Anomaly Detection Based on a Two-Branch 3D Convolutional Autoencoder and Spatial Filtering"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6891-8550","authenticated-orcid":false,"given":"Shuai","family":"Lv","sequence":"first","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Siwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]},{"given":"Dandan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Boyu","family":"Pang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaoying","family":"Lian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yinnian","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"ref_1","first-page":"689","article-title":"Current progress of hyperspectral remote sensing in China","volume":"20","author":"Tong","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"026513","DOI":"10.1117\/1.JRS.13.026513","article-title":"Local hyperspectral anomaly detection method based on low-rank and sparse matrix decomposition","volume":"13","author":"Chang","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4666","DOI":"10.1109\/TGRS.2020.2965961","article-title":"Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection","volume":"58","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph Convolutional Networks for Hyperspectral Image Classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSP.2013.2278992","article-title":"Hyperspectral Target Detection","volume":"31","author":"Nasrabadi","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"031501","DOI":"10.1117\/1.JRS.15.031501","article-title":"Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review","volume":"15","author":"Peyghambari","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"eabf4507","DOI":"10.1126\/sciadv.abf4507","article-title":"Satellite-based survey of extreme methane emissions in the Permian basin","volume":"7","author":"Guanter","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TCYB.2020.2968750","article-title":"Low-Rank and Sparse Decomposition with Mixture of Gaussian for Hyperspectral Anomaly Detection","volume":"51","author":"Li","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bati, E., Caliskan, A., Koz, A., and Alatan, A.A. (2015, January 21\u201323). Hyperspectral Anomaly Detection Method Based on Auto-encoder. Proceedings of the Conference on Image and Signal Processing for Remote Sensing XXI, Toulouse, France.","DOI":"10.1117\/12.2195180"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.sigpro.2015.09.037","article-title":"A spectral-spatial based local summation anomaly detection method for hyperspectral images","volume":"124","author":"Du","year":"2016","journal-title":"Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/TNNLS.2020.3038659","article-title":"Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chang, C.I. (2022). Effective Anomaly Space for Hyperspectral Anomaly Detection. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2022.3161632"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MGRS.2021.3105440","article-title":"Hyperspectral Anomaly Detection: A Survey","volume":"10","author":"Su","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MAES.2010.5546306","article-title":"A Tutorial Overview of Anomaly Detection in Hyperspectral Images","volume":"25","author":"Matteoli","year":"2010","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band cfar detection of an optical-pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/JSTARS.2013.2239959","article-title":"Multiple-Window Anomaly Detection for Hyperspectral Imagery","volume":"6","author":"Liu","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1109\/TGRS.2012.2214392","article-title":"Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection","volume":"51","author":"Matteoli","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/JSTARS.2014.2302446","article-title":"Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery","volume":"7","author":"Guo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/TGRS.2010.2081677","article-title":"Random-Selection-Based Anomaly Detector for Hyperspectral Imagery","volume":"49","author":"Du","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/TGRS.2004.841487","article-title":"Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery","volume":"43","author":"Kwon","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1109\/TGRS.2006.873019","article-title":"A support vector method for anomaly detection in hyperspectral imagery","volume":"44","author":"Banerjee","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.1080\/01431161.2019.1708504","article-title":"Hyperspectral anomaly detection by local joint subspace process and support vector machine","volume":"41","author":"Xiang","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TGRS.2004.841481","article-title":"A cluster-based approach for detecting man-made objects and changes in imagery","volume":"43","author":"Carlotto","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Messinger, D.W., and Albano, J. (2011, January 6\u20139). A graph theoretic approach to anomaly detection in hyperspectral imagery. Proceedings of the 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal.","DOI":"10.1109\/WHISPERS.2011.6080899"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1109\/LGRS.2014.2306209","article-title":"Local Sparsity Divergence for Hyperspectral Anomaly Detection","volume":"11","author":"Yuan","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1109\/TGRS.2018.2872900","article-title":"A Constrained Sparse Representation Model for Hyperspectral Anomaly Detection","volume":"57","author":"Ling","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TGRS.2014.2343955","article-title":"Collaborative Representation for Hyperspectral Anomaly Detection","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"083641","DOI":"10.1117\/1.JRS.8.083641","article-title":"Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery","volume":"8","author":"Sun","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1109\/TGRS.2015.2479299","article-title":"A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2019.2936609","article-title":"Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection","volume":"58","author":"Cheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1109\/TGRS.2015.2493201","article-title":"Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation","volume":"54","author":"Xu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6504","DOI":"10.1109\/TNNLS.2021.3082158","article-title":"Weakly Supervised Discriminative Learning with Spectral Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection","volume":"33","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/LGRS.2017.2657818","article-title":"Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery","volume":"14","author":"Li","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.1109\/JSTARS.2019.2926130","article-title":"Hyperspectral Anomaly Detection via Convolutional Neural Network and Low Rank with Density-Based Clustering","volume":"12","author":"Song","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fan, G.H., Ma, Y., Mei, X.G., Fan, F., Huang, J., and Ma, J.Y. (2022). Hyperspectral Anomaly Detection with Robust Graph Autoencoders. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2021.3097097"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.neunet.2019.08.012","article-title":"Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detection","volume":"119","author":"Xie","year":"2019","journal-title":"Neural Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"042605","DOI":"10.1117\/1.JRS.11.042605","article-title":"Hyperspectral anomaly detection based on stacked denoising autoencoders","volume":"11","author":"Zhao","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1109\/TIP.2017.2658954","article-title":"Curvature Filters Efficiently Reduce Certain Variational Energies","volume":"26","author":"Gong","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2019.2960945","article-title":"Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders","volume":"17","author":"Nalepa","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TCYB.2022.3175771","article-title":"Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection","volume":"53","author":"Wang","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.1117\/1.601039","article-title":"Nonincreasing filters using morphological gradient criteria","volume":"35","year":"1996","journal-title":"Opt. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/JSTARS.2021.3052968","article-title":"Visual Attention and Background Subtraction with Adaptive Weight for Hyperspectral Anomaly Detection","volume":"14","author":"Xiang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"Threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"254","DOI":"10.3788\/gzxb20215004.0410003","article-title":"Hyperspectral Anomaly Detection Based on 3D Convolutional Autoencoder and Law Rank Representation","volume":"50","author":"Sun","year":"2021","journal-title":"Acta Photonica Sin."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"042610","DOI":"10.1117\/1.JRS.15.042610","article-title":"3D autoencoder algorithm for lithological mapping using ZY-1 02D hyperspectral imagery: A case study of Liuyuan region","volume":"15","author":"Yu","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6808","DOI":"10.1109\/TGRS.2019.2908756","article-title":"Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification","volume":"57","author":"Mei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_48","first-page":"25","article-title":"Visible-shortwave Infrared Hyperspectral Imager of GF-5 Satellite","volume":"39","author":"Liu","year":"2018","journal-title":"Spacecr. Recovery Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/JSTARS.2013.2238609","article-title":"Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data","volume":"6","author":"Molero","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:02Z","timestamp":1760124842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,12]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102542"],"URL":"https:\/\/doi.org\/10.3390\/rs15102542","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,5,12]]}}}