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These methods can achieve high accuracy, but most of them are computationally intensive models. This poses a problem for their implementation in low-power and embedded systems intended for on-board processing, in which energy consumption and model size are as important as accuracy. With a focus on embedded and on-board systems (in which only the inference step is performed after an off-line training process), in this paper we provide a comprehensive overview of the inference properties of the most relevant techniques for hyperspectral image classification. For this purpose, we compare the size of the trained models and the operations required during the inference step (which are directly related to the hardware and energy requirements). Our goal is to search for appropriate trade-offs between on-board implementation (such as model size and energy consumption) and classification accuracy.<\/jats:p>","DOI":"10.3390\/rs12030534","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T03:13:27Z","timestamp":1581045207000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Inference in Supervised Spectral Classifiers for On-Board Hyperspectral Imaging: An Overview"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7057-4283","authenticated-orcid":false,"given":"Adri\u00e1n","family":"Alcolea","sequence":"first","affiliation":[{"name":"Computer Architecture Group (gaZ), Department of Computer Science and Systems Engineering, Ada Byron Building, University of Zaragoza, C\/Mar\u00eda de Luna 1, E-50018 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1030-3729","authenticated-orcid":false,"given":"Mercedes E.","family":"Paoletti","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avenida de la Universidad sn, E-10003 Caceres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6701-961X","authenticated-orcid":false,"given":"Juan M.","family":"Haut","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avenida de la Universidad sn, E-10003 Caceres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-2720","authenticated-orcid":false,"given":"Javier","family":"Resano","sequence":"additional","affiliation":[{"name":"Computer Architecture Group (gaZ), Department of Computer Science and Systems Engineering, Ada Byron Building, University of Zaragoza, C\/Mar\u00eda de Luna 1, E-50018 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-1659","authenticated-orcid":false,"given":"Antonio","family":"Plaza","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avenida de la Universidad sn, E-10003 Caceres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liang, S. (2008). Advances in Land Remote Sensing: System, Modeling, Inversion and Application, Springer Science & Business Media.","DOI":"10.1007\/978-1-4020-6450-0_1"},{"key":"ref_2","unstructured":"Liang, S. (2017). Comprehensive Remote Sensing, Elsevier."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gruen, A. (2008). Scientific-technological developments in photogrammetry and remote sensing between 2004 and 2008. Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book, CRC Press.","DOI":"10.1201\/9780203888445.ch2"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xu, H., Wang, Y., Guan, H., Shi, T., and Hu, X. (2019). Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11202345"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.jaridenv.2005.03.032","article-title":"Desertification assessment in China: An overview","volume":"63","author":"Yang","year":"2005","journal-title":"J. Arid Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2018.04.048","article-title":"Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil","volume":"213","author":"Mariano","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.rse.2013.07.008","article-title":"Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier","volume":"139","author":"Grinand","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.rse.2017.10.034","article-title":"Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2","volume":"204","author":"Reiche","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.rse.2017.11.025","article-title":"Quantitative mapping of groundwater depletion at the water management scale using a combined GRACE\/InSAR approach","volume":"205","author":"Castellazzi","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111441","DOI":"10.1016\/j.rse.2019.111441","article-title":"Spatio-temporal pattern of soil degradation in a Swiss Alpine grassland catchment","volume":"235","author":"Zweifel","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, H., Hu, X., Guan, H., Zhang, B., Wang, M., Chen, S., and Chen, M. (2019). A Remote Sensing Based Method to Detect Soil Erosion in Forests. Remote Sens., 11.","DOI":"10.3390\/rs11050513"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gonsamo, A., Ter-Mikaelian, M.T., Chen, J.M., and Chen, J. (2019). Does Earlier and Increased Spring Plant Growth Lead to Reduced Summer Soil Moisture and Plant Growth on Landscapes Typical of Tundra-Taiga Interface?. Remote Sens., 11.","DOI":"10.3390\/rs11171989"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, X., Lee, Z., Zhang, Y., Lin, J., Shi, K., Zhou, Y., Qin, B., and Sun, Z. (2019). Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data. Remote Sens., 11.","DOI":"10.3390\/rs11192226"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kratzer, S., Kyryliuk, D., Edman, M., Philipson, P., and Lyon, S.W. (2019). Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters. Remote Sens., 11.","DOI":"10.3390\/rs11172051"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.05.002","article-title":"Investigating sea surface temperature diurnal variation over the Tropical Warm Pool using MTSAT-1R data","volume":"183","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Du, J., Watts, J.D., Jiang, L., Lu, H., Cheng, X., Duguay, C., Farina, M., Qiu, Y., Kim, Y., and Kimball, J.S. (2019). Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges. Remote Sens., 11.","DOI":"10.3390\/rs11161952"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2017.02.027","article-title":"Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data","volume":"193","author":"He","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Prasad, S., Bruce, L.M., and Chanussot, J. (2011). Optical remote sensing. Advances in Signal Processing and Exploitation Techniques, Springer.","DOI":"10.1007\/978-3-642-14212-3"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging spectrometry for earth remote sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the Earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"112","article-title":"Multi-and hyperspectral geologic remote sensing: A review","volume":"14","author":"Hecker","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","article-title":"Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas","volume":"46","author":"Dalponte","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1016\/j.rse.2007.02.043","article-title":"Remote sensing of native and invasive species in Hawaiian forests","volume":"112","author":"Asner","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/JSTARS.2013.2282166","article-title":"Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms","volume":"7","author":"Shang","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","first-page":"7","article-title":"Remote sensing for mapping natural habitats and their conservation status\u2013New opportunities and challenges","volume":"37","author":"Corbane","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/S0034-4257(03)00096-8","article-title":"Mapping nonnative plants using hyperspectral imagery","volume":"86","author":"Underwood","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.3390\/rs4092510","article-title":"Hyperspectral time series analysis of native and invasive species in Hawaiian rainforests","volume":"4","author":"Somers","year":"2012","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.04.006","article-title":"Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests","volume":"136","author":"Somers","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1109\/JSTARS.2015.2396577","article-title":"Random Forests Unsupervised Classification: The Detection and Mapping of Solanum mauritianum Infestations in Plantation Forestry Using Hyperspectral Data","volume":"8","author":"Peerbhay","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Lin, Q., Huang, H., Wang, J., Huang, K., and Liu, Y. (2019). Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar. Remote Sens., 11.","DOI":"10.3390\/rs11212540"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.rse.2005.08.010","article-title":"Substrate age and precipitation effects on Hawaiian forest canopies from spaceborne imaging spectroscopy","volume":"98","author":"Asner","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, X.M., Wang, N., Wu, H., Tang, B.H., and Li, Z.L. (2011, January 24). Estimation of precipitable water from the thermal infrared hyperspectral data. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049910"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0034-4257(01)00238-3","article-title":"Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data","volume":"79","author":"Koponen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2012.11.023","article-title":"Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: The Mississippi River and its tributaries in Minnesota","volume":"130","author":"Olmanson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s10661-005-9106-4","article-title":"Mapping invasive aquatic vegetation in the Sacramento-San Joaquin Delta using hyperspectral imagery","volume":"121","author":"Underwood","year":"2006","journal-title":"Environ. Monit. Assess."},{"key":"ref_37","first-page":"61","article-title":"Quantitative hyperspectral analysis for characterization of the coastal water from Damietta to Port Said, Egypt","volume":"17","year":"2014","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1080\/014311697218278","article-title":"Mineral mapping with hyperspectral digital imagery collection experiment (HYDICE) sensor data at Cuprite, Nevada, USA","volume":"18","author":"Resmini","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.3390\/rs3081584","article-title":"Effect of reduced spatial resolution on mineral mapping using imaging spectrometry\u2014Examples using Hyperspectral Infrared Imager (HyspIRI)-simulated data","volume":"3","author":"Kruse","year":"2011","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mielke, C., Rogass, C., Boesche, N., Segl, K., and Altenberger, U. (2016). EnGeoMAP 2.0\u2013Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission. Remote Sens., 8.","DOI":"10.3390\/rs8020127"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2017.03.009","article-title":"Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring","volume":"128","author":"Scafutto","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, 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_43","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3844\/ajabssp.2010.50.55","article-title":"A review: The role of remote sensing in precision agriculture","volume":"5","author":"Liaghat","year":"2010","journal-title":"Am. J. Agric. Biol. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2006.05.018","article-title":"Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data","volume":"104","author":"Bannari","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_46","first-page":"229","article-title":"Remote sensing of soil properties in precision agriculture: A review","volume":"5","author":"Ge","year":"2011","journal-title":"Front. Earth Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/JSTARS.2015.2462125","article-title":"Characterization of soil erosion indicators using hyperspectral data from a Mediterranean rainfed cultivated region","volume":"9","author":"Schmid","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","first-page":"295","article-title":"Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing","volume":"4","author":"Zhang","year":"2003","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1080\/01431160310001618031","article-title":"Detecting sugarcane \u2018orange rust\u2019disease using EO-1 Hyperion hyperspectral imagery","volume":"25","author":"Apan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s11119-007-9037-x","article-title":"Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data","volume":"8","author":"Rao","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1016\/j.rse.2009.06.004","article-title":"The influence of urban structures on impervious surface maps from airborne hyperspectral data","volume":"113","author":"Hostert","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.3390\/rs3091817","article-title":"Can the future EnMAP mission contribute to urban applications? A literature survey","volume":"3","author":"Heldens","year":"2011","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.landurbplan.2012.01.001","article-title":"Urban structure type characterization using hyperspectral remote sensing and height information","volume":"105","author":"Heiden","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ardouin, J.P., L\u00e9vesque, J., and Rea, T.A. (2007, January 9\u201312). A demonstration of hyperspectral image exploitation for military applications. Proceedings of the 2007 10th International Conference on Information Fusion, Quebec, ON, Canada.","DOI":"10.1109\/ICIF.2007.4408184"},{"key":"ref_55","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_56","doi-asserted-by":"crossref","unstructured":"Ardouin, J.P., L\u00e9vesque, J., Roy, V., Van Chestein, Y., and Faust, A. (2012). Demonstration of hyperspectral image exploitation for military applications. Remote Sensing-Applications, IntechOpen.","DOI":"10.5772\/37681"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2005.02.002","article-title":"Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards","volume":"59","author":"Tralli","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2018.06.020","article-title":"Hyperspectral remote sensing of fire: State-of-the-art and future perspectives","volume":"216","author":"Veraverbeke","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/JSTARS.2014.2320576","article-title":"A review of nonlinear hyperspectral unmixing methods","volume":"7","author":"Heylen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and classification of hyperspectral images using watershed transformation","volume":"43","author":"Tarabalka","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_62","first-page":"4085","article-title":"Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1109\/TGRS.2011.2128330","article-title":"Hyperspectral image segmentation using a new Bayesian approach with active learning","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/36.934070","article-title":"Best-bases feature extraction algorithms for classification of hyperspectral data","volume":"39","author":"Kumar","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/MSP.2014.2312071","article-title":"Effective feature extraction and data reduction in remote sensing using hyperspectral imaging [applications corner]","volume":"31","author":"Ren","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1109\/TGRS.2002.804721","article-title":"Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction","volume":"40","author":"Bruce","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s11554-018-0793-9","article-title":"Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines","volume":"15","author":"Haut","year":"2018","journal-title":"J. Real-Time Image Process."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1109\/JSTARS.2015.2437073","article-title":"Hyperspectral anomaly detection by the use of background joint sparse representation","volume":"8","author":"Li","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_69","first-page":"708","article-title":"Sparse unmixing-based change detection for multitemporal hyperspectral images","volume":"9","author":"Iordache","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/LGRS.2015.2390973","article-title":"Informative change detection by unmixing for hyperspectral images","volume":"12","author":"Plaza","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"6497","DOI":"10.1109\/TGRS.2016.2585495","article-title":"A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images","volume":"54","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s11554-017-0742-z","article-title":"A real-time unsupervised background extraction-based target detection method for hyperspectral imagery","volume":"15","author":"Li","year":"2018","journal-title":"J. Real-Time Image Process."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"9499","DOI":"10.1109\/TGRS.2019.2927077","article-title":"Portability Study of an OpenCL Algorithm for Automatic Target Detection in Hyperspectral Images","volume":"57","author":"Igual","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced spectral classifiers for hyperspectral images: A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_77","first-page":"45","article-title":"Advances in hyperspectral image classification: Earth monitoring with statistical learning methods","volume":"31","author":"Tuia","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_78","first-page":"214","article-title":"Multinomial logistic regression-based feature selection for hyperspectral data","volume":"14","author":"Pal","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Borges, J.S., Bioucas-Dias, J.M., and Mar\u00e7al, A.R. (2006). Fast Sparse Multinomial Regression Applied to Hyperspectral Data, Springer.","DOI":"10.1007\/11867661_63"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/LGRS.2015.2408433","article-title":"Real-time implementation of the sparse multinomial logistic regression for hyperspectral image classification on GPUs","volume":"12","author":"Wu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","article-title":"Spectral\u2013spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields","volume":"50","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1109\/LGRS.2014.2320258","article-title":"A subspace-based multinomial logistic regression for hyperspectral image classification","volume":"11","author":"Khodadadzadeh","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_83","unstructured":"Bioucas-Dias, J., and Figueiredo, M. (2009). Logistic Regression via Variable Splitting and Augmented Lagrangian Tools, Instituto Superior T\u00e9cnico, TULisbon."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/LGRS.2008.2005512","article-title":"Using suitable neighbors to augment the training set in hyperspectral maximum likelihood classification","volume":"5","author":"Richards","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Waske, B., and Benediktsson, J.A. (2014). Pattern recognition and classification. Encyclopedia of Remote Sensing, Springer.","DOI":"10.1007\/978-0-387-36699-9_69"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"419","DOI":"10.3844\/jcssp.2007.419.423","article-title":"The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis","volume":"3","author":"Kuching","year":"2007","journal-title":"J. Comput. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2993","DOI":"10.1080\/01431160701442070","article-title":"Feature-selection ability of the decision-tree algorithm and the impact of feature-selection\/extraction on decision-tree results based on hyperspectral data","volume":"29","author":"Wang","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.rse.2012.08.029","article-title":"Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers","volume":"126","author":"Delalieux","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_89","unstructured":"Joelsson, S.R., Benediktsson, J.A., and Sveinsson, J.R. (2005, January 29). Random forest classifiers for hyperspectral data. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea."},{"key":"ref_90","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_91","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1023\/A:1007614523901","article-title":"Improved boosting algorithms using confidence-rated predictions","volume":"37","author":"Schapire","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_92","unstructured":"Fu, Z., Caelli, T., Liu, N., and Robles-Kelly, A. (2006, January 24). Boosted band ratio feature selection for hyperspectral image classification. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China."},{"key":"ref_93","unstructured":"Freund, Y., and Schapire, R.E. (1996, January 3\u20136). Experiments with a new boosting algorithm. Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"3845","DOI":"10.1109\/TGRS.2007.903708","article-title":"Hyperspectral image classification by bootstrap AdaBoost with random decision stumps","volume":"45","author":"Kawaguchi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Ul Haq, Q.S., Tao, L., and Yang, S. (2011, January 26). Neural network based adaboosting approach for hyperspectral data classification. Proceedings of the 2011 International Conference on Computer Science and Network Technology, Harbin, China.","DOI":"10.1109\/ICCSNT.2011.6181949"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1109\/JSTARS.2013.2292901","article-title":"Classification of hyperspectral data using an AdaBoostSVM technique applied on band clusters","volume":"7","author":"Ramzi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_97","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_98","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1080\/2150704X.2014.915070","article-title":"Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting","volume":"5","author":"Filippi","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_99","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_100","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_101","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.3390\/rs4113462","article-title":"Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data","volume":"4","author":"Colgan","year":"2012","journal-title":"Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S0168-1699(03)00020-6","article-title":"Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn","volume":"39","author":"Goel","year":"2003","journal-title":"Comput. Electron. Agric."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.compag.2004.11.014","article-title":"Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data","volume":"47","author":"Uno","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep pyramidal residual networks for spectral\u2013spatial hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TGRS.2018.2871782","article-title":"Capsule networks for hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Verleysen, M., and Fran\u00e7ois, D. (2005). The Curse of Dimensionality in Data Mining and Time Series Prediction, Springer.","DOI":"10.1007\/11494669_93"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MSP.2013.2279894","article-title":"Manifold-learning-based feature extraction for classification of hyperspectral data: A review of advances in manifold learning","volume":"31","author":"Lunga","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"6440","DOI":"10.1109\/TGRS.2018.2838665","article-title":"Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1109\/LGRS.2018.2881045","article-title":"Low\u2013High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification","volume":"16","author":"Haut","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/JSTSP.2011.2142490","article-title":"Introduction to the issue on advances in remote sensing image processing","volume":"5","author":"Benediktsson","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Khorram, S., van der Wiele, C.F., Koch, F.H., Nelson, S.A., and Potts, M.D. (2016). Future trends in remote sensing. Principles of Applied Remote Sensing, Springer.","DOI":"10.1007\/978-3-319-22560-9"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.actaastro.2004.09.034","article-title":"NASA\u2019s small satellite missions for Earth observation","volume":"56","author":"Neeck","year":"2005","journal-title":"Acta Astronaut."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.actaastro.2009.06.008","article-title":"Status and trends of small satellite missions for Earth observation","volume":"66","author":"Sandau","year":"2010","journal-title":"Acta Astronaut."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.jpdc.2005.10.001","article-title":"Commodity cluster-based parallel processing of hyperspectral imagery","volume":"66","author":"Plaza","year":"2006","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Bernab\u00e9, S., and Plaza, A. (2011, January 9). Commodity cluster-based parallel implementation of an automatic target generation process for hyperspectral image analysis. Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems, Tainan, Taiwan.","DOI":"10.1109\/ICPADS.2011.45"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/JSTARS.2010.2095495","article-title":"High performance computing for hyperspectral remote sensing","volume":"4","author":"Plaza","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0309133309339563","article-title":"A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters","volume":"33","author":"Joyce","year":"2009","journal-title":"Prog. Phys. Geogr."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1117\/1.602577","article-title":"Real-time hyperspectral detection and cuing","volume":"39","author":"Stellman","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/36.917889","article-title":"Real-time processing algorithms for target detection and classification in hyperspectral imagery","volume":"39","author":"Chang","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1016\/j.patcog.2006.08.006","article-title":"Unsupervised real-time constrained linear discriminant analysis to hyperspectral image classification","volume":"40","author":"Du","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.3390\/rs70403966","article-title":"Global and local real-time anomaly detectors for hyperspectral remote sensing imagery","volume":"7","author":"Zhao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1109\/JSTARS.2019.2917088","article-title":"Real-time hyperspectral image compression onto embedded GPUs","volume":"12","author":"Guerra","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Plaza, A.J., and Chang, C.I. (2007). High Performance Computing in Remote Sensing, CRC Press.","DOI":"10.1201\/9781420011616"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1177\/1094342007088376","article-title":"Clusters versus FPGA for parallel processing of hyperspectral imagery","volume":"22","author":"Plaza","year":"2008","journal-title":"Int. J. High Perform. Comput. Appl."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1007\/s11554-017-0679-2","article-title":"FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images","volume":"16","author":"Li","year":"2019","journal-title":"J. Real-Time Image Process."},{"key":"ref_127","unstructured":"Maurer, P., and Glumb, A.J. (2019). On-Board Processing of Hyperspectral Data. (15\/966,470), U.S. Patent."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"4042","DOI":"10.1109\/TGRS.2009.2025270","article-title":"Calibration of PRISM and AVNIR-2 onboard ALOS \u201cDaichi\u201d","volume":"47","author":"Tadono","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Henriksen, M.B., Garrett, J., Prentice, E.F., Stahl, A., Johansen, T., and Sigernes, F. (2019, January 26). Real-Time Corrections for a Low-Cost Hyperspectral Instrument. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8921350"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"10644","DOI":"10.1109\/ACCESS.2019.2892308","article-title":"Scalable hardware-based on-board processing for run-time adaptive lossless hyperspectral compression","volume":"7","author":"Rodriguez","year":"2019","journal-title":"IEEE Access"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Liu, D., Zhou, G., Huang, J., Zhang, R., Shu, L., Zhou, X., and Xin, C.S. (2019). On-Board Georeferencing Using FPGA-Based Optimized Second-Order Polynomial Equation. Remote Sens., 11.","DOI":"10.3390\/rs11020124"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11554-008-0106-9","article-title":"Fast real-time onboard processing of hyperspectral imagery for detection and classification","volume":"4","author":"Du","year":"2009","journal-title":"J. Real-Time Image Process."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Qi, B., Shi, H., Zhuang, Y., Chen, H., and Chen, L. (2018). On-board, real-time preprocessing system for optical remote-sensing imagery. Sensors, 18.","DOI":"10.3390\/s18051328"},{"key":"ref_134","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer Science + Business Media."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1016\/j.procs.2015.02.140","article-title":"Spatial preprocessing based multinomial logistic regression for hyperspectral image classification","volume":"46","author":"Prabhakar","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_136","unstructured":"Scikit Learn (2019, February 01). Generalized Linear Models. Logistic Regression. Available online: https:\/\/scikit-learn.org\/stable\/modules\/linear_model.html#logistic-regression."},{"key":"ref_137","unstructured":"Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees, Chapman & Hall, Taylor & Francis Group."},{"key":"ref_138","unstructured":"G\u00e9ron, A. (2017). Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media, Inc."},{"key":"ref_139","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_140","unstructured":"Scikit Learn (2019, February 01). Ensemble Methods. Forests of Randomized Trees. Available online: https:\/\/scikit-learn.org\/stable\/modules\/ensemble.html#forest."},{"key":"ref_141","first-page":"1189","article-title":"Greedy function approximation: A gradient boosting machine","volume":"45","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_142","unstructured":"LightGBM (2019, February 01). LightGBM Docs. LGBMClassifier. Available online: https:\/\/lightgbm.readthedocs.io\/en\/latest\/pythonapi\/lightgbm.LGBMClassifier.html."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_144","unstructured":"Scikit Learn (2019, February 01). Support Vector Machines. Mathematical Formulation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/svm.html#svm-mathematical-formulation."},{"key":"ref_145","unstructured":"PyTorch (2019, February 01). PyTorch Docs. Neural Network. Available online: https:\/\/pytorch.org\/docs\/stable\/nn.html#module-torch.nn."},{"key":"ref_146","unstructured":"GIC (2019, February 01). Hyperspectral Remote Sensing Scenes, Grupo de Inteligencia Computacional de la Universidad del Pa\u00eds Vasco. Available online: http:\/\/www.ehu.eus\/ccwintco\/index.php\/Hyperspectral_Remote_Sensing_Scenes."},{"key":"ref_147","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_148","unstructured":"IEEE (2019, February 01). IEEE GRSS Data Fusion Contest. Available online: http:\/\/www.grss-ieee.org\/community\/technical-committees\/data-fusion\/2013-ieee-grss-data-fusion-contest\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:23Z","timestamp":1760172923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,6]]},"references-count":148,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030534"],"URL":"https:\/\/doi.org\/10.3390\/rs12030534","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,6]]}}}