Evaluating training data for crop type classifıcation using support vector machine and random forests
Journal Title: Geodetski glasnik - Year 2017, Vol 51, Issue 48
Abstract
This study evaluated the effectiveness of three different training datasets for crop type classification using both support vector machines (SVMs) and random forests (RFs). In supervised classification, one of the main facing challanges is to define the training set for the full representation of land use/cover classes. The adaptation of traning data, with the implemented classifier and its characteristics (purity, size and distribution of sample pixels), are of key importance in this context. The experimental results were compared in terms of the classification accuracy with 10-fold cross validation. Results suggest that higher classification accuracies were obtained by less number of training samples. Furthermore, it is highlighted that both methods (SVMs and RFs) are proven to be the effective and powerful classifiers for crop type classification.
Authors and Affiliations
Mustafa Ustuner, Fusun Balik Sanli
Keywords
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