{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T21:01:06Z","timestamp":1781989266076,"version":"3.54.5"},"reference-count":48,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Joint Funds of the National Natural Science Foundation of China","award":["U1906217"],"award-info":[{"award-number":["U1906217"]}]},{"name":"General Program of the National Natural Science Foundation of China","award":["62071491"],"award-info":[{"award-number":["62071491"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["19CX05003A-11"],"award-info":[{"award-number":["19CX05003A-11"]}],"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>Lately, generative adversarial networks (GAN)-based methods have drawn extensive attention and achieved a promising performance in the field of hyperspectral anomaly detection (HAD) owing to GAN\u2019s powerful data generation capability. However, without considering the background spatial features, most of these methods can not obtain a GAN with a strong background generation ability. Besides, they fail to address the hyperspectral image (HSI) redundant information disturbance problem in the anomaly detection part. To solve these issues, the unsupervised generative adversarial network with background spatial feature enhancement and irredundant pooling (BEGAIP) is proposed for HAD. To make better use of features, spatial and spectral features union extraction idea is also applied to the proposed model. To be specific, in spatial branch, a new background spatial feature enhancement way is proposed to get a data set containing relatively pure background information to train GAN and reconstruct a more vivid background image. In a spectral branch, irredundant pooling (IP) is invented to remove redundant information, which can also enhance the background spectral feature. Finally, the features obtained from the spectral and spatial branch are combined for HAD. The experimental results conducted on several HSI data sets display that the model proposed acquire a better performance than other relevant algorithms.<\/jats:p>","DOI":"10.3390\/rs14051265","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"1265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Unsupervised Generative Adversarial Network with Background Enhancement and Irredundant Pooling for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhongwei","family":"Li","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunxiao","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leiquan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6758-9863","authenticated-orcid":false,"given":"Mingming","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8160","DOI":"10.1109\/JSTARS.2021.3103744","article-title":"Dual Graph U-Nets for Hyperspectral Image Classification","volume":"14","author":"Guo","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ins.2019.02.008","article-title":"Hyperspectral image unsupervised classification by robust manifold matrix factorization","volume":"485","author":"Zhang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, Z., Cui, X., Wang, L., Zhang, H., Zhu, X., and Zhang, Y. (2021). Spectral and Spatial Global Context Attention for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13040771"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L., and Zhou, H. (2018). Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning. Remote Sens., 10.","DOI":"10.3390\/rs10020339"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSP.2013.2278992","article-title":"Hyperspectral target detection: An overview of current and future challenges","volume":"31","author":"Nasrabadi","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MGRS.2018.2867592","article-title":"Mini-UAV-borne hyperspectral remote sensing: From observation and processing to applications","volume":"6","author":"Zhong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5887","DOI":"10.1109\/JSTARS.2020.3024903","article-title":"Background learning based on target suppression constraint for hyperspectral target detection","volume":"13","author":"Xie","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1109\/JSTARS.2019.2894802","article-title":"Multitask learning-based reliability analysis for hyperspectral target detection","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","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":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.isprsjprs.2020.09.008","article-title":"Low rank and collaborative representation for hyperspectral anomaly detection via robust dictionary construction","volume":"169","author":"Su","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, G., Li, F., Zhang, X., Laakso, K., and Chan, J.C.W. (2021). Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection. Remote Sens., 13.","DOI":"10.3390\/rs13204102"},{"key":"ref_13","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. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/LGRS.2007.903083","article-title":"Kernel eigenspace separation transform for subspace anomaly detection in hyperspectral imagery","volume":"4","author":"Goldberg","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","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."},{"key":"ref_16","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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1109\/TGRS.2018.2872590","article-title":"Hyperspectral anomaly detection via background and potential anomaly dictionaries construction","volume":"57","author":"Huyan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","unstructured":"Alhashim, I., and Wonka, P. (2018). High quality monocular depth estimation via transfer learning. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Demertzis, K., and Iliadis, L. (2020). GeoAI: A model-agnostic meta-ensemble zero-shot learning method for hyperspectral image analysis and classification. Algorithms, 13.","DOI":"10.3390\/a13030061"},{"key":"ref_21","unstructured":"Kendall, A., Gal, Y., and Cipolla, R. (2018, January 18\u201322). Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8131","DOI":"10.1109\/TGRS.2019.2918387","article-title":"Spectral\u2013spatial feature extraction for hyperspectral anomaly detection","volume":"57","author":"Lei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35673","DOI":"10.1109\/ACCESS.2019.2905511","article-title":"Convolutional autoencoder-based multispectral image fusion","volume":"7","author":"Azarang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"5416","DOI":"10.1109\/TGRS.2020.2965995","article-title":"Autoencoder and adversarial-learning-based semisupervised background estimation for hyperspectral anomaly detection","volume":"58","author":"Xie","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19208","DOI":"10.1109\/ACCESS.2021.3054822","article-title":"Constrained Generative Adversarial Networks","volume":"9","author":"Chao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.inffus.2021.09.014","article-title":"Supervised contrastive learning over prototype-label embeddings for network intrusion detection","volume":"79","author":"Arribas","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4218","DOI":"10.1109\/TGRS.2018.2890212","article-title":"Structure tensor and guided filtering-based algorithm for hyperspectral anomaly detection","volume":"57","author":"Xie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE."},{"key":"ref_33","unstructured":"Choi, J., and Han, B. (2021). MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s10115-018-1306-7","article-title":"Variational data generative model for intrusion detection","volume":"60","author":"Carro","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Paul Smolley, S. (2017, January 22\u201329). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1109\/LSP.2020.3027517","article-title":"The role of neural network activation functions","volume":"27","author":"Parhi","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TTE.2020.3032225","article-title":"Detection of TTF in Induction Motor Vector Drives for EV Applications via Ostu\u2019s-Based DDWE","volume":"7","author":"Eldeeb","year":"2020","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1109\/TGRS.2017.2786718","article-title":"Joint reconstruction and anomaly detection from compressive hyperspectral images using Mahalanobis distance-regularized tensor RPCA","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5600","DOI":"10.1109\/TGRS.2017.2710145","article-title":"Hyperspectral anomaly detection with attribute and edge-preserving filters","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, S.Y., Yang, S., Kalpakis, K., and Chang, C.I. (2013, January 18). Low-rank decomposition-based anomaly detection. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, Baltimore, MD, USA.","DOI":"10.1117\/12.2015652"},{"key":"ref_42","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":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","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":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tan, K., Hou, Z., Ma, D., Chen, Y., and Du, Q. (2019). Anomaly detection in hyperspectral imagery based on low-rank representation incorporating a spatial constraint. Remote Sens., 11.","DOI":"10.3390\/rs11131578"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1109\/TGRS.2018.2810124","article-title":"BASO: A background-anomaly component projection and separation optimized filter for anomaly detection in hyperspectral images","volume":"56","author":"Chang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Xiang, P., Song, J., Li, H., Gu, L., and Zhou, H. (2019). Hyperspectral anomaly detection with harmonic analysis and low-rank decomposition. Remote Sens., 11.","DOI":"10.3390\/rs11243028"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1109\/TGRS.2019.2956159","article-title":"Hyperspectral band selection for spectral\u2013spatial anomaly detection","volume":"58","author":"Xie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/JSTARS.2020.2990457","article-title":"Hyperspectral anomaly detection based on low-rank representation with data-driven projection and dictionary construction","volume":"13","author":"Ma","year":"2020","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\/14\/5\/1265\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:32:40Z","timestamp":1760135560000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1265"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,5]]},"references-count":48,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051265"],"URL":"https:\/\/doi.org\/10.3390\/rs14051265","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,5]]}}}