{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T19:01:00Z","timestamp":1785265260213,"version":"3.55.0"},"reference-count":54,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T00:00:00Z","timestamp":1592524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Innovation Promotion Association Foundation of the Chinese Academy of Sciences","award":["2018476"],"award-info":[{"award-number":["2018476"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601440"],"award-info":[{"award-number":["41601440"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013494","name":"West Light Foundation of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2016-QNXZ-B-11"],"award-info":[{"award-number":["2016-QNXZ-B-11"]}],"id":[{"id":"10.13039\/501100013494","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.<\/jats:p>","DOI":"10.3390\/rs12121973","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T10:43:58Z","timestamp":1592563438000},"page":"1973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-6033","authenticated-orcid":false,"given":"Alim","family":"Samat","sequence":"first","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, CAS, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erzhu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geographical Information Science, Jiangsu Normal University, Xuzhou 221100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1813-0551","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, CAS, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1612-4844","authenticated-orcid":false,"given":"Sicong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8483-1554","authenticated-orcid":false,"given":"Jilili","family":"Abuduwaili","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China"},{"name":"Research Center for Ecology and Environment of Central Asia, CAS, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, X. (2011). Limits and challenges of optical high-resolution satellite remote sensing for urban applications. Urban Remote Sensing\u2014Monitoring, Synthesis and Modelling in the Urban Environment, Wiley.","DOI":"10.1002\/9780470979563"},{"key":"ref_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/JSTARS.2014.2301775","article-title":"E2LMs: Ensemble Extreme Learning Machines for Hyperspectral Image Classification","volume":"7","author":"Samat","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1109\/JSTARS.2018.2824354","article-title":"Classification of VHR multispectral images using extratrees and maximally stable extremal region-guided morphological profile","volume":"11","author":"Samat","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","first-page":"503","article-title":"Quad-PolSAR data classification using modified random forest algorithms to map halophytic plants in arid areas","volume":"73","author":"Samat","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Samat, A., Yokoya, N., Du, P., Liu, S., Ma, L., Ge, Y., and Lin, C. (2019). Direct, ECOC, ND and END Frameworks\u2014Which One Is the Best? An Empirical Study of Sentinel-2A MSIL1C Image Classification for Arid-Land Vegetation Mapping in the Ili River Delta, Kazakhstan. Remote Sens., 11.","DOI":"10.3390\/rs11161953"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.patcog.2015.08.019","article-title":"Improved hyperspectral image classification by active learning using pre-designed mixed pixels","volume":"51","author":"Samat","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s41651-020-00048-5","article-title":"Advances of Four Machine Learning Methods for Spatial Data Handling: A Review","volume":"4","author":"Du","year":"2020","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1109\/TPAMI.2013.159","article-title":"Learning nonlinear functions using regularized greedy forest","volume":"36","author":"Johnson","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","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":"Ann. Stat."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ye, J., Chow, J.H., Chen, J., and Zheng, Z. (2009, January 2\u20136). Stochastic gradient boosted distributed decision trees. Proceedings of the 18th ACM Conference on INFORMATION and Knowledge management, Hongkong, China.","DOI":"10.1145\/1645953.1646301"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1109\/TITS.2016.2635719","article-title":"Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method","volume":"18","author":"Ma","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"Natekin","year":"2013","journal-title":"Front. Neurorobot."},{"key":"ref_23","unstructured":"Zheng, Z., Zha, H., Zhang, T., Chapelle, O., Chen, K., and Sun, G. (2008, January 12). A general boosting method and its application to learning ranking functions for web search. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s11069-015-1915-3","article-title":"Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: Application to the 2009 storm event in Messina (Sicily, southern Italy)","volume":"79","author":"Lombardo","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_25","first-page":"145","article-title":"Multi-class image classification based on fast stochastic gradient boosting","volume":"38","author":"Lin","year":"2014","journal-title":"Informatica"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3659","DOI":"10.1016\/j.eswa.2011.09.058","article-title":"Gradient boosting trees for auto insurance loss cost modeling and prediction","volume":"39","author":"Guelman","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2004.01.007","article-title":"Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis","volume":"90","author":"Lawrence","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ecolmodel.2006.05.021","article-title":"Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods","volume":"199","author":"Moisen","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_30","first-page":"87","article-title":"Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery","volume":"25","author":"Chirici","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1139\/cjfr-2014-0562","article-title":"Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance","volume":"46","author":"Freeman","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.14778\/1687553.1687569","article-title":"Planet: Massively parallel learning of tree ensembles with mapreduce","volume":"2","author":"Panda","year":"2009","journal-title":"Proc. Vldb Endow."},{"key":"ref_33","unstructured":"Meng, Q., Ke, G., Wang, T., Chen, W., Ye, Q., Ma, Z.M., and Liu, T. (2016, January 5\u201310). A communication-efficient parallel algorithm for decision tree. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1109\/TPDS.2016.2603511","article-title":"A parallel random forest algorithm for big data in a spark cloud computing environment","volume":"28","author":"Chen","year":"2016","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_35","unstructured":"Abuzaid, F., Bradley, J.K., Liang, F.T., Feng, A., Yang, L., Zaharia, M., and Talwalkar, A.S. (2016, January 5\u201310). Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_36","unstructured":"Zhang, H., Si, S., and Hsieh, C.J. (2017). GPU-acceleration for Large-scale Tree Boosting. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dong, H., Xu, X., Wang, L., and Pu, F. (2018). Gaofen-3 PolSAR image classification via XGBoost and polarimetric spatial information. Sensors, 18.","DOI":"10.3390\/s18020611"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2020.138229","article-title":"Exploring the relationship between 2D\/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China","volume":"725","author":"Yu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, H., Eziz, A., Xiao, J., Tao, S., Wang, S., Tang, Z., and Fang, J. (2019). High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features. Remote Sens., 11.","DOI":"10.3390\/rs11121505"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.atmosenv.2019.01.027","article-title":"Extreme gradient boosting model to estimate PM2.5 concentrations with missing filled satellite data in China","volume":"202","author":"Chen","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1010852229904","article-title":"An adaptive version of the boost by majority algorithm","volume":"43","author":"Freund","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_42","unstructured":"Rashmi, K.V., and Gilad-Bachrach, R. (2015, January 9\u201312). DART: Dropouts meet Multiple Additive Regression Trees. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, CA, USA."},{"key":"ref_43","unstructured":"Bradley, J.K., Kyrola, A., Bickson, D., and Guestrin, C. (2011). Parallel coordinate descent for l1-regularized loss minimization. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","unstructured":"Chen, T., He, T., and Benesty, M. xgboost: Extreme Gradient Boosting, R Foundation for Statistical Computing. R Package Version 0.3-0; Technical Report."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","unstructured":"Donoser, M., and Bischof, H. (2006, January 17\u201322). Efficient maximally stable extremal region (MSER) tracking. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Forss\u00e9n, P.E. (2007, January 17\u201322). Maximally stable colour regions for recognition and matching. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383120"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.7717\/peerj-cs.127","article-title":"Accelerating the XGBoost algorithm using GPU computing","volume":"3","author":"Mitchell","year":"2017","journal-title":"Peerj Comput. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2014.2305441","article-title":"Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest","volume":"7","author":"Debes","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1023\/A:1007515423169","article-title":"An empirical comparison of voting classification algorithms: Bagging, boosting, and variants","volume":"36","author":"Bauer","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_52","first-page":"549","article-title":"MultiBoost: A multi-purpose boosting package","volume":"13","author":"Benbouzid","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/TGRS.2015.2488280","article-title":"Morphological attribute profiles with partial reconstruction","volume":"54","author":"Liao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2663666","article-title":"Taking Optimal Advantage of Fine Spatial Resolution: Promoting partial image reconstruction for the morphological analysis of very-high-resolution images","volume":"5","author":"Liao","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1973\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:40:58Z","timestamp":1760175658000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,19]]},"references-count":54,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12121973"],"URL":"https:\/\/doi.org\/10.3390\/rs12121973","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,19]]}}}