{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:15:59Z","timestamp":1779200159570,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"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":["41971388"],"award-info":[{"award-number":["41971388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, the methods based on the autoencoder reconstruction background have been applied to the area of hyperspectral image (HSI) anomaly detection (HSI-AD). However, the encoding mechanism of the autoencoder (AE) makes it possible to treat the anomaly and the background indistinguishably during reconstruction, which can result in a small number of anomalous pixels still being included in the acquired reconstruction background. In addition, the problem of redundant information in HSIs also exists in reconstruction errors. To this end, a fully convolutional AE hyperspectral anomaly detection (AD) network with an attention gate (AG) connection is proposed. First, the low-dimensional feature map as a product of the encoder and the fine feature map as a product of the corresponding decoding stage are simultaneously input into the AG module. The network context information is used to suppress the irrelevant regions in the input image and obtain the significant feature map. Then, the features from the AG and the deep features from upsampling are efficiently combined in the decoder stage based on the skip connection to gradually estimate the reconstructed background image. Finally, post-processing optimization based on guided filtering (GF) is carried out on the reconstruction error to eliminate the wrong anomalous pixels in the reconstruction error image and amplify the contrast between the anomaly and the background.<\/jats:p>","DOI":"10.3390\/rs15174263","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:09:49Z","timestamp":1693390189000},"page":"4263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["FCAE-AD: Full Convolutional Autoencoder Based on Attention Gate for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7600-9939","authenticated-orcid":false,"given":"Xianghai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Liaoning Normal University, Dalian 116029, China"},{"name":"School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8117-7889","authenticated-orcid":false,"given":"Yihan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhua","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ben Salem, M., Ettabaa, K.S., and Hamdi, M.A. (2014, January 5\u20137). Anomaly Detection in Hyperspectral Imagery: An Overview. Proceedings of the International Image Processing, Applications and Systems Conference, Sfax, Tunisia.","DOI":"10.1109\/IPAS.2014.7043320"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Racetin, I., and Krtali\u0107, A. (2021). Systematic review of anomaly detection in hyperspectral remote sensing applications. Appl. Sci., 11.","DOI":"10.3390\/app11114878"},{"key":"ref_3","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_4","first-page":"1878","article-title":"A Density-Based Cluster Kernel RX Algorithm for Hyperspectral Anomaly Detection","volume":"39","author":"Hao","year":"2019","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Z., and Zhang, Y. (August, January 28). Hyperspectral Anomaly Detection Based on Improved RX with CNN Framework. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898327"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1109\/LGRS.2019.2942949","article-title":"A superpixel-based dual window RX for hyperspectral anomaly detection","volume":"17","author":"Ren","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, J., Song, S., and Liu, D. (2019). Hyperspectral anomaly detection via dictionary construction-based low-rank representation and adaptive weighting. Remote Sens., 11.","DOI":"10.3390\/rs11020192"},{"key":"ref_8","first-page":"6008305","article-title":"Enhance Tensor RPCA-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection","volume":"19","author":"Ruhan","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1109\/LGRS.2019.2943861","article-title":"A low-rank and sparse matrix decomposition-based dictionary reconstruction and anomaly extraction framework for hyperspectral anomaly detection","volume":"17","author":"Xu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107718","DOI":"10.1016\/j.sigpro.2020.107718","article-title":"Self-weighted collaborative representation for hyperspectral anomaly detection","volume":"177","author":"Wang","year":"2020","journal-title":"Signal Process."},{"key":"ref_11","first-page":"5533417","article-title":"Hyperspectral anomaly detection with relaxed collaborative representation","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112305","DOI":"10.1007\/s11432-020-2915-2","article-title":"Collaborative representation with background purification and saliency weight for hyperspectral anomaly detection","volume":"65","author":"Hou","year":"2022","journal-title":"Sci. China Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kuswidiyanto, L.W., Noh, H.H., and Han, X. (2022). Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review. Remote Sens., 14.","DOI":"10.3390\/rs14236031"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_15","first-page":"1","article-title":"Deep learning\u2014Based text classification: A comprehensive review","volume":"54","author":"Minaee","year":"2021","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_16","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_17","first-page":"220","article-title":"Hyperspectral anomaly detection method based on auto-encoder","volume":"9643","author":"Bati","year":"2015","journal-title":"Image Signal Process. Remote Sens. XXI. Spie"},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1109\/TGRS.2019.2944419","article-title":"Exploiting embedding manifold of autoencoders for hyperspectral anomaly detection","volume":"58","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, L., and Frey, B. (2015). Adversarial autoencoders. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1109\/TIP.2013.2244222","article-title":"Image fusion with guided filtering","volume":"22","author":"Li","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1016\/j.neucom.2017.11.038","article-title":"Efficient coarse-to-fine spectral rectification for hyperspectral image","volume":"275","author":"Xie","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","article-title":"Spectral\u2013spatial hyperspectral image classification with edge-preserving filtering","volume":"52","author":"Kang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_27","first-page":"5503314","article-title":"Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yu, M., Chen, X., Zhang, W., and Liu, Y. (2022). AGs-Unet: Building Extraction Model for High Resolution Remote Sensing Images Based on Attention Gates U Network. Sensors, 22.","DOI":"10.3390\/s22082932"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5538818","DOI":"10.1109\/TGRS.2022.3207165","article-title":"Hyperspectral anomaly detection with guided autoencoder","volume":"60","author":"Xiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mu, Z., Wang, M., Wang, Y., Song, R., and Wang, X. (2023). SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection. Remote Sens., 15.","DOI":"10.3390\/rs15030612"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/LGRS.2020.2998809","article-title":"Hyperspectral anomaly detection via integration of feature extraction and background purification","volume":"18","author":"Ma","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Shang, W., Jouni, M., Wu, Z., and Xu, Y. (2023). Hyperspectral Anomaly Detection Based on Regularized Background Abundance Tensor Decomposition. Remote Sens., 15.","DOI":"10.3390\/rs15061679"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4263\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:43:12Z","timestamp":1760128992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,30]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174263"],"URL":"https:\/\/doi.org\/10.3390\/rs15174263","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,30]]}}}