  Pixel based algorithms including back propagation neural networks (NN) and
support vector machines (SVM) have been widely used for remotely sensed image
classifications. Within last few years, deep learning based image classifier
like convolution neural networks (2D-CNN) are becoming popular alternatives to
these classifiers. In this paper, we compare performance of patch based SVM and
NN with that of a deep learning algorithms comprising of 2D-CNN and fully
connected layers. Similar to CNN which utilise image patches to derive features
for further classification, we propose to use patches as an input in place of
individual pixel with both SVM and NN classifiers. Two datasets, one
multispectral and other hyperspectral data was used to compare the performance
of different classifiers. Results with both datasets suggest the effectiveness
of patch based SVM and NN classifiers in comparison to state of art 2D-CNN
classifier.
