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APPLICATION OF SUPPORT VECTOR MACHINES FOR FODDER CROP ASSESSMENT
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
<p><strong>Abstract.</strong> Identification of crop and its accuracy is an important aspect in predicting crop production using Remote Sensing technology. This study investigates the ability of Support Vector Machine (SVM) algorithm in discriminating fodder crops and estimating its area using moderate resolution multi-temporal Landsat-8 OLI data. SVM is a non-parametric statistical learning method and its accuracy is dependent on the parameters and the kernels used. The objective was todoi:10.5194/isprs-annals-iv-5-415-2018 fatcat:abh5mae3fbbufo3o6rkvqpvctq