Classification of Hyperspectral Data Using a Multi-Channel Convolutional Neural Network

Abstract

In recent years, deep learning is widely used for hyperspectral image (HSI) classification, among them, convolutional neural network (CNN) is most popular. In this paper, we propose a method for hyperspectral data classification by multi-channel convolutional neural network (MC-CNN). In this framework, one dimensional CNN (1D-CNN) is mainly used to extract the spectral feature of hyperspectral images, two dimension CNN (2D-CNN) is mainly used to extract the spatial feature of hyperspectral images, three-dimensional CNN (3D-CNN) is mainly used to extract part of the spatial and spectral information. And then these features are merged and pull into the full connection layer. At last, using neural network classifiers like logistic regression, we can eventually get class labels for each pixel. For comparison and validation, we compare the proposed MC-CNN algorithm with the other three deep learning algorithms. Experimental results show that our MC-CNN-based algorithm outperforms these state-of-the-art algorithms. Showcasing the MC-CNN framework has huge potential for accurate hyperspectral data classification.