{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T17:21:37Z","timestamp":1732036897609},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.<\/jats:p>","DOI":"10.3390\/s17092007","type":"journal-article","created":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T15:05:24Z","timestamp":1504278324000},"page":"2007","source":"Crossref","is-referenced-by-count":34,"title":["Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1893-6301","authenticated-orcid":false,"given":"Thomas","family":"Alexandridis","sequence":"first","affiliation":[{"name":"Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece"}]},{"given":"Afroditi Alexandra","family":"Tamouridou","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece"},{"name":"Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece"}]},{"given":"Xanthoula Eirini","family":"Pantazi","sequence":"additional","affiliation":[{"name":"Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece"}]},{"given":"Anastasia","family":"Lagopodi","sequence":"additional","affiliation":[{"name":"Plant Pathology Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece"}]},{"given":"Javid","family":"Kashefi","sequence":"additional","affiliation":[{"name":"USDA-ARS-European Biological Control Laboratory, Tsimiski 43, 7th floor, Thessaloniki 54623, Greece"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3225-8033","authenticated-orcid":false,"given":"Georgios","family":"Ovakoglou","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece"}]},{"given":"Vassilios","family":"Polychronos","sequence":"additional","affiliation":[{"name":"Geosense S.A., Filikis Etairias 15-17, Pylaia, Thessaloniki 55535, Greece"}]},{"given":"Dimitrios","family":"Moshou","sequence":"additional","affiliation":[{"name":"Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11119-004-5321-1","article-title":"A review on remote sensing of weeds in agriculture","volume":"5","author":"Thorp","year":"2004","journal-title":"Precis. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.isprsjprs.2012.02.006","article-title":"Robust hyperspectral vision-based classification for multi-season weed mapping","volume":"69","author":"Zhang","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1023\/A:1015679903293","article-title":"An agricultural mobile robot with vision-based perception for mechanical weed control","volume":"13","author":"Baerveldt","year":"2002","journal-title":"Auton. Robots"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1109\/TGRS.2010.2041784","article-title":"Sensitivity of support vector machines to random feature selection in classification of hyperspectral data","volume":"48","author":"Waske","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.cropro.2012.04.024","article-title":"Classification of crops and weeds from digital images: A support vector machine approach","volume":"40","author":"Ahmed","year":"2012","journal-title":"Crop Prot."},{"key":"ref_6","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, John Wiley & Sons, Inc."},{"key":"ref_7","unstructured":"Sluiter, R. (2005). Mediterranean Land Cover Change: Modelling and Monitoring Natural Vegetation Using Gis and Remote Sensing, Utrecht University."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2005.09.008","article-title":"A change detection model based on neighborhood correlation image analysis and decision tree classification","volume":"99","author":"Im","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8549","DOI":"10.1080\/01431161.2013.845317","article-title":"Remote sensing of tea plantations using an svm classifier and pattern-based accuracy assessment technique","volume":"34","author":"Dihkan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","first-page":"1137","article-title":"Neural-network-based system for novel fault detection in rotating machinery","volume":"10","author":"Crupi","year":"2004","journal-title":"Modal Anal."},{"key":"ref_11","unstructured":"Tax, D.M.J. (2001). One-Class Classification. [Ph.D. Thesis, Delft University of Technology]."},{"key":"ref_12","unstructured":"Parsons, W.T., and Cuthbertson, E. (2001). Noxious Weeds of Australia, CSIRO Publishing."},{"key":"ref_13","unstructured":"Tucker, J.M., Cordy, D.R., Berry, L.J., Harvey, W.A., and Fuller, T.C. (1961). Nitrate Poisoning in Livestock, University of California."},{"key":"ref_14","first-page":"73","article-title":"Screening the allelopathic potential of various weeds","volume":"11","author":"Khan","year":"2011","journal-title":"Pak. J. Weed Sci. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.1080\/01431161.2016.1252475","article-title":"Evaluation of uav imagery for mapping silybum marianum weed patches","volume":"38","author":"Tamouridou","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.compag.2017.05.026","article-title":"Evaluation of hierarchical self-organising maps for weed mapping using uas multispectral imagery","volume":"139","author":"Pantazi","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pe\u00f1a, J.M., Torres-S\u00e1nchez, J., de Castro, A.I., Kelly, M., and L\u00f3pez-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0077151"},{"key":"ref_18","unstructured":"Rojas, I., Joya, G., and Catala, A. (2015, January 6). An experimental comparison for the identification of weeds in sunflower crops via unmanned aerial vehicles and object-based analysis. Proceedings of the 13th International Work-Conference on Artificial Neural Networks (IWANN), Palma de Mallorca, Spain."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., L\u00f3pez-Granados, F., De Castro, A.I., and Pe\u00f1a-Barrag\u00e1n, J.M. (2013). Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0058210"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","article-title":"Support vector data description","volume":"54","author":"Tax","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_21","unstructured":"Scholkopf, B., and Smola, A.J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Saunders, R., and Gero, J.S. (2001, January 19\u201321). A curious design agent: A computational model of novelty-seeking behaviour in design. Proceedings of the Sixth Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2001), Sydney, Australia.","DOI":"10.52842\/conf.caadria.2001.345"},{"key":"ref_23","unstructured":"Japkowicz, N., Myers, C., and Gluck, M. (1995). A Novelty Detection Approach to Classification, International Joint Conference on Artificial Intelligence (IJCAI 95)."},{"key":"ref_24","unstructured":"Hertz, J.A., Krogh, A.S., and Palmer, R.G. (1991). Introduction to the Theory of Neural Computation, Addison-Wesley Longman Publishing Co., Inc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0893-6080(89)90014-2","article-title":"Neural networks and principal component analysis: Learning from examples without local minima","volume":"2","author":"Baldi","year":"1989","journal-title":"Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/BF00332918","article-title":"Auto-association by multilayer perceptrons and singular value decomposition","volume":"59","author":"Bourlard","year":"1988","journal-title":"Biol. Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Metternicht, G. (2007). Geospatial Technologies and the Management of Noxious Weeds in Agricultural and Rangelands Areas of Australia, University of South Australia."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/2007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T11:47:29Z","timestamp":1717847249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/2007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,1]]},"references-count":29,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["s17092007"],"URL":"https:\/\/doi.org\/10.3390\/s17092007","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,1]]}}}