{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T19:01:15Z","timestamp":1769367675469,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"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":["61903279"],"award-info":[{"award-number":["61903279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZH22017003200010PWC"],"award-info":[{"award-number":["ZH22017003200010PWC"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhuhai Basic and Applied Basic Research Foundation","award":["61903279"],"award-info":[{"award-number":["61903279"]}]},{"name":"Zhuhai Basic and Applied Basic Research Foundation","award":["ZH22017003200010PWC"],"award-info":[{"award-number":["ZH22017003200010PWC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral target detection is one of the most challenging tasks in remote sensing due to limited spectral information. Many algorithms based on matrix decomposition (MD) are proposed to promote the separation of the background and targets, but they suffer from two problems: (1) Targets are detected with the criterion of reconstruction residuals, and the imbalanced number of background and target atoms in union dictionary may lead to misclassification of targets. (2) The detection results are susceptible to the quality of the apriori target spectra, thus obtaining inferior performance because of the inevitable spectral variability. In this paper, we propose a matrix decomposition-based detector named dictionary learning-cooperated matrix decomposition (DLcMD) for hyperspectral target detection. The procedure of DLcMD is two-fold. First, the low rank and sparse matrix decomposition (LRaSMD) is exploited to separate targets from the background due to its insensitivity to the imbalanced number of background and target atoms, which can reduce the misclassification of targets. Inspired by dictionary learning, the target atoms are updated during LRaSMD to alleviate the impact of spectral variability. After that, a binary hypothesis model specifically designed for LRaSMD is proposed, and a generalized likelihood ratio test (GLRT) is performed to obtain the final detection result. Experimental results on five datasets have shown the reliability of the proposed method. Especially in the Los Angeles-II dataset, the area under the curve (AUC) value is nearly 16% higher than the average value of the other seven detectors, which reveals the superiority of DLcMD in hyperspectral target detection.<\/jats:p>","DOI":"10.3390\/rs14174369","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Dictionary Learning-Cooperated Matrix Decomposition for Hyperspectral Target Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuan","family":"Yao","sequence":"first","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"},{"name":"Hubei Branch of The National Internet Emergency Center of China, Wuhan 430074, China"}]},{"given":"Mengbi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Precision Instrument, Tsinghua University, Beijing 100084, China"}]},{"given":"Ganghui","family":"Fan","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Wendi","family":"Liu","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Yong","family":"Ma","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0239-8580","authenticated-orcid":false,"given":"Xiaoguang","family":"Mei","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1016\/j.neucom.2017.11.052","article-title":"Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation","volume":"275","author":"Mei","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jin, Q., Ma, Y., Pan, E., Fan, F., Huang, J., Li, H., Sui, C., and Mei, X. (2019). Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity. Remote Sens., 11.","DOI":"10.3390\/rs11202434"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TGRS.2016.2616649","article-title":"Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neucom.2021.07.015","article-title":"ContrastNet: Unsupervised feature learning by autoencoder and prototypical contrastive learning for hyperspectral imagery classification","volume":"460","author":"Cao","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4758","DOI":"10.1109\/TGRS.2019.2892899","article-title":"Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters From Remotely Sensed Multispectral and Hyperspectral Imagery","volume":"57","author":"Xu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/JSTARS.2018.2814617","article-title":"Multiparameter Optimization for Mineral Mapping Using Hyperspectral Imagery","volume":"11","author":"Li","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Adao, T., Hruska, J., Padua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_8","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":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neucom.2021.08.130","article-title":"Spectral mapping with adversarial learning for unsupervised hyperspectral change detection","volume":"465","author":"Lei","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6059","DOI":"10.1109\/TGRS.2020.2972289","article-title":"CSVM architectures for pixel-wise object detection in high-resolution remote sensing images","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Karoui, M.S., Benhalouche, F.Z., Deville, Y., Djerriri, K., Briottet, X., Houet, T., Le Bris, A., and Weber, C. (2019). Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data. Remote Sens., 11.","DOI":"10.3390\/rs11182164"},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"6844","DOI":"10.1109\/TGRS.2014.2303895","article-title":"A Discriminative Metric Learning Based Anomaly Detection Method","volume":"52","author":"Du","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107464","DOI":"10.1016\/j.patcog.2020.107464","article-title":"Data-augmented matched subspace detector for hyperspectral subpixel target detection","volume":"106","author":"Yang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/TSP.2020.2977458","article-title":"A Dictionary-Based Generalization of Robust PCA with Applications to Target Localization in Hyperspectral Imaging","volume":"68","author":"Rambhatla","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, G., Zhao, S., Li, W., Du, Q., Ran, Q., and Tao, R. (2020). HTD-Net: A Deep Convolutional Neural Network for Target Detection in Hyperspectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12091489"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/S0034-4257(96)00080-6","article-title":"Mapping the distribution of mine tailings in the Coeur d\u2019Alene River Valley, Idaho, through the use of a constrained energy minimization technique","volume":"59","author":"Farrand","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/TGRS.2015.2456957","article-title":"Hierarchical Suppression Method for Hyperspectral Target Detection","volume":"54","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3138","DOI":"10.1117\/1.1327499","article-title":"Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images","volume":"39","author":"Ren","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_22","first-page":"79","article-title":"Hyperspectral image processing for automatic target detection applications","volume":"14","author":"Manolakis","year":"2003","journal-title":"J. Linc. Lab."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Manolakis, D., Shaw, G., and Keshava, N. (2000, January 24\u201326). Comparative analysis of hyperspectral adaptive matched filter detectors. Proceedings of the Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, Orlando, FL, USA.","DOI":"10.1117\/12.410332"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/JSTSP.2011.2113170","article-title":"Sparse representation for target detection in hyperspectral imagery","volume":"5","author":"Chen","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1109\/LGRS.2019.2908196","article-title":"Multi-task Joint Sparse and Low-rank Representation Target Detection for Hyperspectral Image","volume":"16","author":"Wu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, W., Zhang, M., Tao, R., and Ma, P. (2020). Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection. Remote Sens., 12.","DOI":"10.3390\/rs12233991"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1109\/TGRS.2020.2995775","article-title":"Single-Spectrum-Driven Binary-Class Sparse Representation Target Detector for Hyperspectral Imagery","volume":"59","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Li, X., Wright, J., Cand\u00e8s, E.J., and Ma, Y. (2010, January 13\u201318). Stable principal component pursuit. Proceedings of the IEEE ISIT, Austin, TX, USA.","DOI":"10.1109\/ISIT.2010.5513535"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/83.913593","article-title":"Efficient detection in hyperspectral imagery","volume":"10","author":"Schweizer","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1109\/TIP.2016.2601268","article-title":"Beyond the Sparsity-Based Target Detector: A Hybrid Sparsity and Statistics-Based Detector for Hyperspectral Images","volume":"25","author":"Du","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1109\/TGRS.2014.2337883","article-title":"A sparse representation-based binary hypothesis model for target detection in hyperspectral images","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"733402","DOI":"10.1117\/12.816917","article-title":"Is there a best hyperspectral detection algorithm?","volume":"7334","author":"Manolakis","year":"2009","journal-title":"Proc. SPIE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TGRS.2013.2246837","article-title":"Sparse Transfer Manifold Embedding for Hyperspectral Target Detection","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","unstructured":"Zhou, T., and Tao, D. (July, January 28). GoDec: Randomized Lowrank and Sparse Matrix Decomposition in Noisy Case. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/TGRS.2016.2616161","article-title":"Robust Sparse Hyperspectral Unmixing with \u21132,1 Norm","volume":"55","author":"Ma","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4996","DOI":"10.1109\/TIP.2013.2281420","article-title":"Infrared Patch-Image Model for Small Target Detection in a Single Image","volume":"22","author":"Gao","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","unstructured":"Wright, J., Ganesh, A., Rao, S., Peng, Y., and Ma, Y. (2009, January 7\u201310). Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Vancouver, BC, Canada."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_40","unstructured":"Lin, Z., Liu, R., and Su, Z. (2011, January 12\u201314). Linearized alternating direction method with adaptive penalty for low rank representation. Proceedings of the Advances in Neural Information Processing Systems 24 (NIPS 2011), Granada, Spain."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/TAES.1986.310745","article-title":"An Adaptive Detection Algorithm","volume":"22","author":"Kelly","year":"1986","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1109\/TGRS.2014.2375351","article-title":"Robust Hyperspectral Image Target Detection Using an Inequality Constraint","volume":"53","author":"Yang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1109\/JSTARS.2014.2320281","article-title":"An Automatic Robust Iteratively Reweighted Unstructured Detector for Hyperspectral Imagery","volume":"7","author":"Wang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1109\/TGRS.2016.2628085","article-title":"Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary","volume":"55","author":"Niu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1137\/080738970","article-title":"A singular value thresholding algorithm for matrix completion","volume":"20","author":"Cai","year":"2010","journal-title":"SIAM J. Optim."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","article-title":"Robust Recovery of Subspace Structures by Low-Rank Representation","volume":"35","author":"Liu","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1109\/78.301849","article-title":"Matched subspace detectors","volume":"42","author":"Scharf","year":"1994","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_49","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 Observ. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/JSTARS.2021.3049843","article-title":"Decomposition Model with Background Dictionary Learning for Hyperspectral Target Detection","volume":"14","author":"Cheng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4369\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:22:37Z","timestamp":1760142157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,2]]},"references-count":51,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174369"],"URL":"https:\/\/doi.org\/10.3390\/rs14174369","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,2]]}}}