ES-CNN: An End-to-End Siamese Convolutional Neural Network for Hyperspectral Image Classification

Abstract

In recent years, deep learning-based methods have achieved great success in remote sensing image analysis. However, especially in the context of hyperspectral image classification, there is still a lack of labelled samples to feed those data-hungry deep models. To augment the amount of input data, models operate on pixel-pairs have been proposed and Siamese convolutional neural network (S-CNN) is a typical one. S-CNN is used as a pixel-pair feature extractor and an additional classifier like SVM is required. In this paper, we propose an end-to-end version of S-CNN. Taking advantage of the pairwise input and to make better use of spatial information, a voting strategy using neighbouring pixels is also employed to determine the final class label of the centre pixel. Experimental results on real hyperspectral datasets show that the proposed method outperforms the original S-CNN by a considerable margin.