Patch Based Classification of Remote Sensing Data: A Comparison of 2D-CNN, SVM and NN Classifiers
This work addresses classification accuracy for remote sensing applications, but it is incremental as it adapts existing methods with patches.
The paper compared patch-based SVM and NN classifiers with a 2D-CNN for remote sensing image classification, finding that patch-based SVM and NN were effective and competitive with the state-of-the-art 2D-CNN on multispectral and hyperspectral datasets.
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.