{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:12:02Z","timestamp":1774627922792,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFC1405605"],"award-info":[{"award-number":["2017YFC1405605"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671037"],"award-info":[{"award-number":["61671037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4192034"],"award-info":[{"award-number":["4192034"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Defense Science and Technology Innovation Special Zone Project","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence\/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of \u201ccascaded detection\u201d, \u201crandom averaging\u201d and \u201cmulti-scale scanning\u201d are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.<\/jats:p>","DOI":"10.3390\/rs11111310","type":"journal-article","created":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T02:08:40Z","timestamp":1559527720000},"page":"1310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":122,"title":["Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-0206","authenticated-orcid":false,"given":"Rui","family":"Zhao","sequence":"first","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1774-552X","authenticated-orcid":false,"given":"Zhengxia","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7816-672X","authenticated-orcid":false,"given":"Zhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85150I","DOI":"10.1117\/12.929037","article-title":"False-alarm characterization in hyperspectral gas-detection applications","volume":"8515","author":"DiPietro","year":"2012","journal-title":"Imaging Spectrom. XVII"},{"key":"ref_2","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_3","first-page":"730","article-title":"An assessment of independent component analysis for detection of military targets from hyperspectral images","volume":"13","author":"Tiwari","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Winter, E.M., Miller, M.A., Simi, C.G., Hill, A.B., Williams, T.J., Hampton, D., Wood, M., Zadnick, J., and Sviland, M.D. (2004, January 21). Mine detection experiments using hyperspectral sensors. Proceedings of the Defense and Security, 2004, Orlando, FL, USA.","DOI":"10.1117\/12.548087"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.isprsjprs.2018.05.022","article-title":"Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques","volume":"142","author":"Lin","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7321","DOI":"10.1007\/s12517-014-1757-4","article-title":"Application of target detection algorithms to identification of iron oxides using ASTER images: A case study in the North of Semnan province, Iran","volume":"8","author":"Rahimzadegan","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1109\/JPROC.2009.2013561","article-title":"Automated hyperspectral cueing for civilian search and rescue","volume":"97","author":"Eismann","year":"2009","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/18.857802","article-title":"An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis","volume":"46","author":"Chang","year":"2000","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_10","first-page":"79","article-title":"Hyperspectral image processing for automatic target detection applications","volume":"14","author":"Manolakis","year":"2003","journal-title":"Lincoln Lab. J."},{"key":"ref_11","first-page":"733402","article-title":"Is there a best hyperspectral detection algorithm?","volume":"7334","author":"Manolakis","year":"2009","journal-title":"SPIE"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.neucom.2017.06.068","article-title":"Matched shrunken subspace detectors for hyperspectral target detection","volume":"272","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_14","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_15","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_16","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1049\/el.2010.0857","article-title":"Robust high-order matched filter for hyperspectral target detection","volume":"46","author":"Shi","year":"2010","journal-title":"Electron. Lett."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1109\/36.298007","article-title":"Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach","volume":"32","author":"Harsanyi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1109\/JSTARS.2013.2254470","article-title":"Hyperspectral unmixing on GPUs and multi-core processors: A comparison","volume":"6","author":"Bernabe","year":"2013","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_20","unstructured":"Kay, S.M. (1993). Fundamentals of Statistical Signal Processing, Prentice Hall PTR."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116401","DOI":"10.1117\/1.2125487","article-title":"Realistic matched filter performance prediction for hyperspectral target detection","volume":"44","author":"Manolakis","year":"2005","journal-title":"Opt. Eng."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1109\/18.857796","article-title":"Hyperspectral imagery: Clutter adaptation in anomaly detection","volume":"46","author":"Schweizer","year":"2000","journal-title":"IEEE Trans. Inf. Theory"},{"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":"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_26","doi-asserted-by":"crossref","unstructured":"Banerjee, A., Burlina, P., and Meth, R. (2007, January 16\u201319). Fast hyperspectral anomaly detection via SVDD. Proceedings of the 2007 IEEE International Conference on Image Processing, San Antonio, TX, USA.","DOI":"10.1109\/ICIP.2007.4379964"},{"key":"ref_27","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_28","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_29","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.infrared.2018.05.025","article-title":"Multi-resolution Networks for Ship Detection in Infrared Remote Sensing Images","volume":"92","author":"Zhou","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/TIP.2017.2773199","article-title":"Random access memories A new paradigm for target detection in high resolution aerial remote sensing images","volume":"27","author":"Zou","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5832","DOI":"10.1109\/TGRS.2016.2572736","article-title":"Ship detection in spaceborne optical image with SVD networks","volume":"54","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, H., Shi, Z., and Zou, Z. (2017). Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network. Remote Sens., 9.","DOI":"10.3390\/rs9050480"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shi, T., Xu, Q., Zou, Z., and Shi, Z. (2018). Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network. Remote Sens., 10.","DOI":"10.3390\/rs10071130"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3623","DOI":"10.1109\/TGRS.2017.2677464","article-title":"Can a Machine Generate Humanlike Language Descriptions for a Remote Sensing Image?","volume":"55","author":"Shi","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1109\/TGRS.2017.2776321","article-title":"Exploring models and data for remote sensing image caption generation","volume":"56","author":"Lu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","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 Obs. Remote Sen."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2012). Ensemble Methods: Foundations and Algorithms, Chapman and Hall\/CRC.","DOI":"10.1201\/b12207"},{"key":"ref_39","first-page":"1612","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"J. Jpn. Soc. Artif. Intell."},{"key":"ref_40","unstructured":"Ho, T.K. (1995, January 14\u201316). Random decision forests. Document analysis and recognition, 1995. Proceedings of the third international conference on, Montreal, QC, Canada."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Barandiaran","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Annal. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zou, Z., Shi, Z., Wu, J., and Wang, H. (2015, January 31). Quadratic constrained energy minimization for hyperspectral target detection. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326950"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H., and Feng, J. (2017). Deep forest: Towards an alternative to deep neural networks. arXiv.","DOI":"10.24963\/ijcai.2017\/497"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2015). Ensemble learning. Encyclopedia of Biometrics, Springer.","DOI":"10.1007\/978-1-4899-7488-4_293"},{"key":"ref_47","unstructured":"Clark, R.N., Swayze, G.A., Gallagher, A.J., King, T.V., and Calvin, W.M. (1993). The US Geological Survey, Digital Spectral Library: Version 1 (0.2 to 3.0 um), Technical Report."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"69661P","DOI":"10.1117\/12.777717","article-title":"How to design synthetic images to validate and evaluate hyperspectral imaging algorithms","volume":"6966","author":"Chang","year":"2008","journal-title":"Proc. SPIE"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging spectroscopy and the airborne visible\/infrared imaging spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_50","unstructured":"(2019, February 07). AVIRIS Cuprite Data, Available online: https:\/\/aviris.jpl.nasa.gov\/data\/free_data.html."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Clark, R.N., Swayze, G.A., Livo, K.E., Kokaly, R.F., Sutley, S.J., Dalton, J.B., McDougal, R.R., and Gent, C.A. (2003). Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems. J. Geophys. Res. Planets, 108.","DOI":"10.1029\/2002JE001847"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1109\/TGRS.2010.2098413","article-title":"Sparse unmixing of hyperspectral data","volume":"49","author":"Iordache","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1310\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:55:22Z","timestamp":1760187322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,1]]},"references-count":52,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11111310"],"URL":"https:\/\/doi.org\/10.3390\/rs11111310","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,1]]}}}