{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T08:01:53Z","timestamp":1781856113406,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["62171404"],"award-info":[{"award-number":["62171404"]}]},{"name":"National Nature Science Foundation of China","award":["LQ21F030017"],"award-info":[{"award-number":["LQ21F030017"]}]},{"name":"Nature Science Foundation of Zhejiang Province","award":["62171404"],"award-info":[{"award-number":["62171404"]}]},{"name":"Nature Science Foundation of Zhejiang Province","award":["LQ21F030017"],"award-info":[{"award-number":["LQ21F030017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As an unsupervised data representation neural network, auto-encoder (AE) has shown great potential in denoising, dimensionality reduction, and data reconstruction. Many AE-based background (BKG) modeling methods have been developed for hyperspectral anomaly detection (HAD). However, their performance is subject to their unbiased reconstruction of BKG and target pixels. This article presents a rather different low rank and sparse matrix decomposition (LRaSMD) method based on AE, named auto-encoder and independent target (AE-IT), for hyperspectral anomaly detection. First, the encoder weight matrix, obtained by a designed AE network, is utilized to construct a projector for generating a low-rank component in the encoder subspace. By adaptively and reasonably determining the number of neurons in the latent layer, the designed AE-based method can promote the reconstruction of BKG. Second, to ensure independence and representativeness, the component in the encoder orthogonal subspace is made into a sphere and followed by finding of unsupervised targets to construct an anomaly space. In order to mitigate the influence of noise on anomaly detection, sparse cardinality (SC) constraint is enforced on the component in the anomaly space for obtaining the sparse anomaly component. Finally, anomaly detector is constructed by combining Mahalanobi distance and multi-components, which include encoder component and sparse anomaly component, to detect anomalies. The experimental results demonstrate that AE-IT performs competitively compared to the LRaSMD-based models and AE-based approaches.<\/jats:p>","DOI":"10.3390\/rs15225266","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:44:18Z","timestamp":1699317858000},"page":"5266","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hyperspectral Anomaly Detection with Auto-Encoder and Independent Target"],"prefix":"10.3390","volume":"15","author":[{"given":"Shuhan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaorun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfeng","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,7]]},"reference":[{"key":"ref_1","first-page":"5511720","article-title":"Hyperspectral anomaly detection: A dual theory of hyperspectral target detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/LGRS.2013.2257670","article-title":"A locally adaptive background density estimator: An evolution for RX-based anomaly detectors","volume":"11","author":"Matteoli","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"5943","DOI":"10.1109\/JSTARS.2022.3191725","article-title":"Subfeature Ensemble-Based Hyperspectral Anomaly Detection Algorithm","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6007505","DOI":"10.1109\/LGRS.2022.3156057","article-title":"A hyperspectral anomaly detection algorithm using sub-features grouping and binary accumulation","volume":"19","author":"Yuan","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1109\/JSTARS.2022.3172120","article-title":"Self-adaptive low-rank and sparse decomposition for hyperspectral anomaly detection","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","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_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"5512216","DOI":"10.1109\/TGRS.2021.3098814","article-title":"Local spatial constraint and total variation for hyperspectral anomaly detection","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","first-page":"5518312","article-title":"Enhanced total variation regularized representation model with endmember background dictionary for hyperspectral anomaly detection","volume":"60","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"1027","article-title":"Robust principal component analysis?","volume":"58","author":"Candes","year":"2009","journal-title":"J. ACM"},{"key":"ref_14","unstructured":"Zhou, T., and Tao, D. (July, January 28). GoDec: Randomized low-rank & sparsity matrix decomposition in noisy case. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, WA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2020.3002724","article-title":"Orthogonal subspace projection-based GoDec for low rank and sparsity matrix decomposition for hyperspectral anomaly detection","volume":"59","author":"Chang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1109\/JSTARS.2021.3068983","article-title":"Orthogonal subspace projection target detector for hyperspectral anomaly detection","volume":"14","author":"Chang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","first-page":"5516222","article-title":"Component Decomposition Analysis for Hyperspectral Anomaly Detection","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Lin, S., Zhang, M., Cheng, X., Wang, L., Xu, M., and Wang, H. (2022). Hyperspectral anomaly detection via dual dictionaries construction guided by two-stage complementary decision. Remote Sens., 14.","DOI":"10.3390\/rs14081784"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/TGRS.2020.3004478","article-title":"Total variation and sparsity regularized decomposition model with union dictionary for hyperspectral anomaly detection","volume":"59","author":"Cheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5542916","DOI":"10.1109\/TGRS.2022.3218826","article-title":"Kernel-Based Decomposition Model with Total Variation and Sparsity Regularizations via Union Dictionary for Nonlinear Hyperspectral Anomaly Detection","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/JSTARS.2022.3214508","article-title":"Dual Collaborative Constraints Regularized Low-Rank and Sparse Representation via Robust Dictionaries Construction for Hyperspectral Anomaly Detection","volume":"16","author":"Lin","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Xiang, P., Li, H., Song, J., Wang, D., Zhang, J., and Zhou, H. (2022). Spectral\u2013spatial complementary decision fusion for hyperspectral anomaly detection. Remote Sens., 14.","DOI":"10.3390\/rs14040943"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cheng, X., Wen, M., Gao, C., and Wang, Y. (2022). Hyperspectral anomaly detection based on wasserstein distance and spatial filtering. Remote Sens., 14.","DOI":"10.3390\/rs14122730"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.infrared.2018.06.001","article-title":"Spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection","volume":"92","author":"Zhao","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5511314","DOI":"10.1109\/TGRS.2021.3097097","article-title":"Hyperspectral anomaly detection with robust graph autoencoders","volume":"60","author":"Fan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"5503314","article-title":"Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","first-page":"5527017","article-title":"Deep Low-Rank Prior for Hyperspectral Anomaly Detection","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","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_35","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_36","first-page":"4139","article-title":"LREN: Low-rank embedded network for sample-free hyperspectral anomaly detection","volume":"35","author":"Jiang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/JSTARS.2017.2782706","article-title":"A review of virtual dimensionality for hyperspectral imagery","volume":"11","author":"Chang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TGRS.2012.2237554","article-title":"A theory of high order statistics-based virtual dimensionality for hyperspectral imagery","volume":"52","author":"Chang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5579","DOI":"10.1109\/TSP.2007.901645","article-title":"Rank estimation and redundancy reduction of high-dimensional noisy signals with preservation of rare vectors","volume":"55","author":"Kuybeda","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.1109\/TGRS.2020.3021671","article-title":"An effective evaluation tool for hyperspectral target detection: 3D receiver operating characteristic curve analysis","volume":"59","author":"Chang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5541124","DOI":"10.1109\/TGRS.2022.3211786","article-title":"Comprehensive Analysis of Receiver Operating Characteristic (ROC) Curves for Hyperspectral Anomaly Detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TGRS.2014.2343955","article-title":"Collaborative representation for hyperspectral anomaly detection","volume":"53","author":"Wei","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","first-page":"5526624","article-title":"Effective anomaly space for hyperspectral anomaly detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"5540428","article-title":"Target-to-anomaly conversion for hyperspectral anomaly detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5504330","DOI":"10.1109\/TGRS.2023.3247660","article-title":"Iterative Spectral-Spatial Hyperspectral Anomaly Detection","volume":"61","author":"Chang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5266\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:18:32Z","timestamp":1760131112000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,7]]},"references-count":45,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15225266"],"URL":"https:\/\/doi.org\/10.3390\/rs15225266","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,7]]}}}