{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:15:16Z","timestamp":1781154916690,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T00:00:00Z","timestamp":1547510400000},"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":["41671360"],"award-info":[{"award-number":["41671360"]}],"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>Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/rs11020150","type":"journal-article","created":{"date-parts":[[2019,1,16]],"date-time":"2019-01-16T03:09:13Z","timestamp":1547608153000},"page":"150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1368-1621","authenticated-orcid":false,"given":"Xing","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Cen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_2","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":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.patcog.2013.07.005","article-title":"Target detection based on a dynamic subspace","volume":"47","author":"Du","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/TGRS.2003.813704","article-title":"A comparative study for orthogonal subspace projection and constrained energy minimization","volume":"41","author":"Du","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"79","article-title":"Hyperspectral image processing for automatic target detection applications","volume":"14","author":"Manolakis","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TGRS.2017.2767068","article-title":"A hybrid sparsity and distance-based discrimination detector for hyperspectral images","volume":"56","author":"Lu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/LGRS.2010.2099640","article-title":"Simultaneous joint sparsity model for target detection in hyperspectral imagery","volume":"8","author":"Chen","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1109\/LGRS.2017.2732454","article-title":"Spatially adaptive sparse representation for target detection in hyperspectral images","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/TIP.2016.2545248","article-title":"Hyperspectral image target detection improvement based on total variation","volume":"25","author":"Yang","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in spectral-spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/JPROC.2012.2229082","article-title":"Feature mining for hyperspectral image classification","volume":"101","author":"Jia","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/LGRS.2014.2337957","article-title":"A new sparsity-based band selection method for target detection of hyperspectral image","volume":"12","author":"Sun","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/LGRS.2005.846011","article-title":"On the impact of pca dimension reduction for hyperspectral detection of difficult targets","volume":"2","author":"Farrell","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.1080\/01431161.2010.512425","article-title":"Extended profiles with morphological attribute filters for the analysis of hyperspectral data","volume":"31","author":"Benediktsson","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art","volume":"5","author":"Ghamisi","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5122","DOI":"10.1109\/TGRS.2013.2286953","article-title":"Remotely sensed image classification using sparse representations of morphological attribute profiles","volume":"52","author":"Song","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5923","DOI":"10.1109\/TGRS.2013.2274875","article-title":"Joint collaborative representation with multitask learning for hyperspectral image classification","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TGRS.2014.2358934","article-title":"A survey on spectral-spatial classification techniques based on attribute profiles","volume":"53","author":"Ghamisi","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1109\/LGRS.2017.2739821","article-title":"Independent encoding joint sparse representation and multitask learning for hyperspectral target detection","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.neucom.2018.06.006","article-title":"Attribute profile based target detection using collaborative and sparse representation","volume":"313","author":"Imani","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/36.905239","article-title":"A new approach for the morphological segmentation of high-resolution satellite imagery","volume":"39","author":"Pesaresi","year":"2001","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/2086737.2086742","article-title":"Learning incoherent sparse and low-rank patterns from multiple tasks","volume":"5","author":"Chen","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, X., Pan, W., Kwok, J.T., and Carbonell, J.G. (2009, January 6\u20139). Accelerated gradient method for multi-task sparse learning problem. Proceedings of the 2009 9th IEEE International Conference on Data Mining, Miami, FL, USA.","DOI":"10.1109\/ICDM.2009.128"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ji, S., and Ye, J. (2009, January 14\u201318). An accelerated gradient method for trace norm minimization. Proceedings of the International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553434"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1109\/TGRS.2010.2048116","article-title":"Morphological attribute profiles for the analysis of very high resolution images","volume":"48","author":"Benediktsson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/TIP.2017.2694222","article-title":"Revisiting co-saliency detection: A novel approach based on two-stage multi-view spectral rotation co-clustering","volume":"26","author":"Yao","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1109\/TCSVT.2017.2706264","article-title":"A unified metric learning-based framework for co-saliency detection","volume":"28","author":"Han","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/150\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:26:04Z","timestamp":1760185564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,15]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020150"],"URL":"https:\/\/doi.org\/10.3390\/rs11020150","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,15]]}}}