{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:49:26Z","timestamp":1770274166566,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T00:00:00Z","timestamp":1524182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI)","award":["3447"],"award-info":[{"award-number":["3447"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,4,20]]},"DOI":"10.1145\/3220228.3220242","type":"proceedings-article","created":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T14:00:37Z","timestamp":1535637637000},"page":"137-141","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine learning classification methods in hyperspectral data processing for agricultural applications"],"prefix":"10.1145","author":[{"given":"Jon\u00e1\u0161","family":"Hru\u0161ka","sequence":"first","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e Alto, Douro, Quinta de Prados, Vila Real"}]},{"given":"Telmo","family":"Ad\u00e3o","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e, Alto Douro, Quinta de Prados, Vila Real"}]},{"given":"Lu\u00eds","family":"P\u00e1dua","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e Alto, Douro, Quinta de Prados, Vila Real"}]},{"given":"Pedro","family":"Marques","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e Alto, Douro, Quinta de Prados, Vila Real"}]},{"given":"Ant\u00f3nio","family":"Cunha","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e, Alto Douro, Quinta de Prados, Vila Real"}]},{"given":"Emanuel","family":"Peres","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e, Alto Douro, Quinta de Prados, Vila Real"}]},{"given":"Ant\u00f3nio","family":"Sousa","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e, Alto Douro, Quinta de Prados, Vila Real"}]},{"given":"Raul","family":"Morais","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e, Alto Douro, Quinta de Prados, Vila Real"}]},{"given":"Joaquim J.","family":"Sousa","sequence":"additional","affiliation":[{"name":"University of Tr\u00e1s-os-Montes e, Alto Douro, Quinta de Prados, Vila Real"}]}],"member":"320","published-online":{"date-parts":[[2018,4,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCUBEA.2015.139"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1108\/SR-07-2016-0124"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2616418"},{"key":"e_1_3_2_1_4_1","volume-title":"2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (Aug. 2015)","author":"Poojary N.","unstructured":"Poojary , N. et al. 2015. Automatic target detection in hyperspectral image processing: A review of algorithms . 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (Aug. 2015) , 1991--1996. Poojary, N. et al. 2015. Automatic target detection in hyperspectral image processing: A review of algorithms. 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (Aug. 2015), 1991--1996."},{"key":"e_1_3_2_1_5_1","volume-title":"Classification of hyperspectral data using spectral-spatial approaches","author":"Tarabalka Y.","unstructured":"Tarabalka , Y. 201 0. Classification of hyperspectral data using spectral-spatial approaches . Institut National Polytechnique de Grenoble-INPG . Tarabalka, Y. 2010. Classification of hyperspectral data using spectral-spatial approaches. Institut National Polytechnique de Grenoble-INPG."},{"key":"e_1_3_2_1_6_1","volume-title":"From early child development to human development: Investing in our children's future","author":"Young M.E.","unstructured":"Young , M.E. 2002. From early child development to human development: Investing in our children's future . World Bank Publications . Young, M.E. 2002. From early child development to human development: Investing in our children's future. World Bank Publications."},{"key":"e_1_3_2_1_7_1","first-page":"2","article-title":"Advances in Remote Sensing of Agriculture: Context Description","volume":"5","author":"Atzberger C.","year":"2013","unstructured":"Atzberger , C. 2013 . Advances in Remote Sensing of Agriculture: Context Description , Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing. 5 , 2 (Feb. 2013), 949--981. Atzberger, C. 2013. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing. 5, 2 (Feb. 2013), 949--981.","journal-title":"Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3844\/ajabssp.2010.50.55"},{"key":"e_1_3_2_1_9_1","volume-title":"Land use and land cover classification using deep learning techniques","author":"Uba N.K.","unstructured":"Uba , N.K. 2016. Land use and land cover classification using deep learning techniques . Arizona State University . Uba, N.K. 2016. Land use and land cover classification using deep learning techniques. Arizona State University."},{"key":"e_1_3_2_1_10_1","volume-title":"The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation","author":"Elarab M.","unstructured":"Elarab , M. 2015. The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation . Utah State University . Elarab, M. 2015. The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation. Utah State University."},{"key":"e_1_3_2_1_11_1","volume-title":"et al","author":"Karalasa K.","year":"2015","unstructured":"Karalasa , K. et al . 2015 . Deep learning for multi-label land cover classification. SPIE Remote Sensing ( 2015), 96430Q---96430Q. Karalasa, K. et al. 2015. Deep learning for multi-label land cover classification. SPIE Remote Sensing (2015), 96430Q---96430Q."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2015.2457631"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2012.2190811"},{"key":"e_1_3_2_1_14_1","unstructured":"Shippert P. 2003. Introduction to hyperspectral image analysis. Online Journal of Space Communication. 3 (2003) 13.  Shippert P. 2003. Introduction to hyperspectral image analysis. Online Journal of Space Communication . 3 (2003) 13."},{"key":"e_1_3_2_1_15_1","first-page":"79","article-title":"Hyperspectral image processing for automatic target detection applications","volume":"14","author":"Manolakis D.","year":"2003","unstructured":"Manolakis , D. 2003 . Hyperspectral image processing for automatic target detection applications . Lincoln Laboratory Journal. 14 , 1 (2003), 79 -- 116 . Manolakis, D. et al. 2003. Hyperspectral image processing for automatic target detection applications. Lincoln Laboratory Journal. 14, 1 (2003), 79--116.","journal-title":"Lincoln Laboratory Journal."},{"key":"e_1_3_2_1_16_1","volume-title":"et al","author":"Yang M.","year":"2015","unstructured":"Yang , M. et al . 2015 . Compressive hyperspectral imaging via adaptive sampling and dictionary learning. arXiv:1512.00901 {cs}. (Dec. 2015). Yang, M. et al. 2015. Compressive hyperspectral imaging via adaptive sampling and dictionary learning. arXiv:1512.00901 {cs}. (Dec. 2015)."},{"key":"e_1_3_2_1_17_1","unstructured":"Geoinformatics|DigitalTextbookLibrary:2008. http:\/\/www.tankonyvtar.hu\/en\/tartalom\/tamop425\/0032_terinformatika\/ch04s04.html. Accessed: 2018-02-06.  Geoinformatics|DigitalTextbookLibrary:2008. http:\/\/www.tankonyvtar.hu\/en\/tartalom\/tamop425\/0032_terinformatika\/ch04s04.html . Accessed: 2018-02-06."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs9111110"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings \/ 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013: Bruges, Belgium, April 24 - 25 - 26","author":"Verleysen M.","year":"2013","unstructured":"Verleysen , M. et al. eds. 2013 . Proceedings \/ 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013: Bruges, Belgium, April 24 - 25 - 26 , 2013 . Ciaco. Verleysen, M. et al. eds. 2013. Proceedings \/ 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013: Bruges, Belgium, April 24 - 25 - 26, 2013. Ciaco."},{"key":"e_1_3_2_1_20_1","unstructured":"Adaptive Control Processes: 1961. https:\/\/press.princeton.edu\/titles\/101.html. Accessed: 2018-01-14.  Adaptive Control Processes: 1961. https:\/\/press.princeton.edu\/titles\/101.html . Accessed: 2018-01-14."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.06.031"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the ASPRS 2001 annual conference","author":"Alonso M.C.","year":"2011","unstructured":"Alonso , M.C. et al. 2011. Consequences of the Hughes phenomenon on some classification techniques . Proceedings of the ASPRS 2001 annual conference ( 2011 ), 1--5. Alonso, M.C. et al. 2011. Consequences of the Hughes phenomenon on some classification techniques. Proceedings of the ASPRS 2001 annual conference (2011), 1--5."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2279179"},{"key":"e_1_3_2_1_24_1","volume-title":"et al","author":"Plaza A.","year":"2009","unstructured":"Plaza , A. et al . 2009 . Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment. 113, (Sep. 2009), S110--S122. Plaza, A. et al. 2009. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment. 113, (Sep. 2009), S110--S122."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.06.009"},{"key":"e_1_3_2_1_26_1","volume-title":"et al","author":"Salimi A.","year":"2017","unstructured":"Salimi , A. et al . 2017 . Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification. The Egyptian Journal of Remote Sensing and Space Science . (Mar. 2017). Salimi, A. et al. 2017. Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification. The Egyptian Journal of Remote Sensing and Space Science. (Mar. 2017)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Shafri H.Z.M. 2016. Machine Learning in Hyperspectral and Multispectral Remote Sensing Data Analysis. Artificial Intelligence Science and Technology. WORLD SCIENTIFIC. 3--9.  Shafri H.Z.M. 2016. Machine Learning in Hyperspectral and Multispectral Remote Sensing Data Analysis. Artificial Intelligence Science and Technology . WORLD SCIENTIFIC. 3--9.","DOI":"10.1142\/9789813206823_0001"},{"key":"e_1_3_2_1_28_1","volume-title":"Machine Learning","author":"Mitchell T.M.","unstructured":"Mitchell , T.M. 1997. Machine Learning . McGraw-Hill, Inc. Mitchell, T.M. 1997. Machine Learning. McGraw-Hill, Inc."},{"key":"e_1_3_2_1_29_1","unstructured":"Huang X. and Jensen J.R. 1997. A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data. Photogrammetric engineering and remote sensing. 63 10 (1997) 1185--1193.  Huang X. and Jensen J.R. 1997. A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data. Photogrammetric engineering and remote sensing . 63 10 (1997) 1185--1193."},{"key":"e_1_3_2_1_30_1","first-page":"1","article-title":"Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines","volume":"1","author":"Linden S.","year":"2007","unstructured":"Linden , S. van der et al. 2007 . Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines . Journal of Applied Remote Sensing. 1 , 1 (Oct. 2007), 013543. Linden, S. van der et al. 2007. Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines. Journal of Applied Remote Sensing. 1, 1 (Oct. 2007), 013543.","journal-title":"Journal of Applied Remote Sensing."},{"key":"e_1_3_2_1_31_1","unstructured":"Shang X. 2013. Evaluating the capability of machine-learning algorithms and object-oriented classification techniques using hyperspectral remote sensing for the discrimination of Australian native forest species in southeastern Australia. University of Wollongong Thesis Collection 1954--2016. (Jan. 2013).  Shang X. 2013. Evaluating the capability of machine-learning algorithms and object-oriented classification techniques using hyperspectral remote sensing for the discrimination of Australian native forest species in southeastern Australia. University of Wollongong Thesis Collection 1954--2016 . (Jan. 2013)."},{"key":"e_1_3_2_1_32_1","volume-title":"et al","author":"Honkavaara E.","year":"2012","unstructured":"Honkavaara , E. et al . 2012 . HYPERSPECTRAL REFLECTANCE SIGNATURES AND POINT CLOUDS FOR PRECISION AGRICULTURE BY LIGHT WEIGHT UAV IMAGING SYSTEM. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. I- 7, (Jul. 2012), 353--358. Honkavaara, E. et al. 2012. HYPERSPECTRAL REFLECTANCE SIGNATURES AND POINT CLOUDS FOR PRECISION AGRICULTURE BY LIGHT WEIGHT UAV IMAGING SYSTEM. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. I-7, (Jul. 2012), 353--358."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPTA.2016.7820963"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2636241"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2540798"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.11.042601"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"e_1_3_2_1_39_1","unstructured":"10\n    Breakthrough Technologies 2013: 2013. https:\/\/www.technologyreview.com\/lists\/technologies\/2013\/.Accessed: 2018-01-15.  10 Breakthrough Technologies 2013: 2013. https:\/\/www.technologyreview.com\/lists\/technologies\/2013\/ .Accessed: 2018-01-15."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/Agro-Geoinformatics.2017.8046996"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2010.03.003"},{"key":"e_1_3_2_1_42_1","volume-title":"IFAC Proceedings Volumes. 46","author":"Yeh Y.","year":"2013","unstructured":"Yeh , Y. - H.F. et al. 2013. A Comparison of Machine Learning Methods on Hyperspectral Plant Disease Assessments . IFAC Proceedings Volumes. 46 , 4 ( 2013 ), 361--365. Yeh, Y.-H.F. et al. 2013. A Comparison of Machine Learning Methods on Hyperspectral Plant Disease Assessments. IFAC Proceedings Volumes. 46, 4 (2013), 361--365."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2015.10.015"},{"key":"e_1_3_2_1_44_1","volume-title":"Proceedings of the 4th International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications-Volume 41","author":"Dutta R.","year":"2015","unstructured":"Dutta , R. et al. 2015. Interactive visual big data analytics for large area farm biosecurity monitoring: i-EKbase system . Proceedings of the 4th International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications-Volume 41 ( 2015 ), 9--18. Dutta, R. et al. 2015. Interactive visual big data analytics for large area farm biosecurity monitoring: i-EKbase system. Proceedings of the 4th International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications-Volume 41 (2015), 9--18."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.4236\/ajps.2017.812219"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2575360"},{"key":"e_1_3_2_1_47_1","volume-title":"et al","author":"Ronneberger O.","year":"2015","unstructured":"Ronneberger , O. et al . 2015 . U-Net: Convolutional Networks for Biomedical Image Segmentation . arXiv:1505.04597 {cs}. (May 2015). Ronneberger, O. et al. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597 {cs}. (May 2015)."}],"event":{"name":"ICGDA '18: 2018 the International Conference on Geoinformatics and Data Analysis, ICGDA '18","location":"Prague Czech Republic","acronym":"ICGDA '18"},"container-title":["Proceedings of the International Conference on Geoinformatics and Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3220228.3220242","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3220228.3220242","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:39:31Z","timestamp":1750210771000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3220228.3220242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,20]]},"references-count":47,"alternative-id":["10.1145\/3220228.3220242","10.1145\/3220228"],"URL":"https:\/\/doi.org\/10.1145\/3220228.3220242","relation":{},"subject":[],"published":{"date-parts":[[2018,4,20]]},"assertion":[{"value":"2018-04-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}