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Pixel based algorithms including back propagation neural networks (NN) andsupport vector machines (SVM) have been widely used for remotely sensed imageclassifications. Within last few years, deep learning based image classifierlike convolution neural networks (2D-CNN) are becoming popular alternatives tothese classifiers. In this paper, we compare performance of patch based SVM andNN with that of a deep learning algorithms comprising of 2D-CNN and fullyconnected layers. Similar to CNN which utilise image patches to derive featuresfor further classification, we propose to use patches as an input in place ofindividual pixel with both SVM and NN classifiers. Two datasets, onemultispectral and other hyperspectral data was used to compare the performanceof different classifiers. Results with both datasets suggest the effectivenessof patch based SVM and NN classifiers in comparison to state of art 2D-CNNclassifier.

Patch Based Classification of Remote Sensing Data: A Comparison of 2D-CNN, SVM and NN Classifiers