Identific ation of Agricultural Crop Types in Northern Israel using Multitemporal RapidEye Data

Summary: Accurate land use / land cover classifi-cation (LU/LC) of agricultural crops still repre-sents a major challenge for multispectral remotesensing. In order to obtain reliable classificationaccuracies on the basis of multispectral satellitedata,mergingcropclassesinratherbroadclassesisoftennecessary.Withregardtotherising availabil-ity and the improving spatial resolution of satellitedata, multitemporal analyses become increasinglyimportant for remote sensing investigations. Forthe separation of spectrally similar crops, multi-datesatelliteimagesinclude differentgrowthchar-acteristics duringthephenologicalperiod.Thepre-sent study aims at investigating a way to performhighlyaccurateclassificationswithnumerousagri-cultural classesusing multitemporalRapidEyedata. The Jeffries-Matusita separability (JM) wasused for applying a pre-procedure in order to findthe best multitemporal setting of all available im-ages withinone crop cycle, consisting of twoculti-vation periods P1 with 16 agricultural classes andP2 with 27 agricultural classes. Only one criticalclass pairing occurred for both P1 and P2 takinginto account the best multitemporal dataset. Themaximum likelihood (ML) classifier and the sup-port vector machine (SVM) were compared usingthemostsuitable multitemporalimages.Bothalgo-rithms achieved very high overall accuracies(OAA)of over 90%. SVM was slightly better witha classification accuracy of P1-OAA = 96.13% andP2-OAA=94.01%. MLprovidedaresult of OAA =94.83% correctly classified pixels for P1 and OAA= 93.28% for P2. Theprocessingtimeof ML,how-ever, was significantly shorter compared to SVM,infact by a factor of five.