Deep Convolutional Neural Networks for Hyperspectral Image Classification

TLDR

Convolutional neural networks have shown excellent performance on various visual tasks, including classification of common two‑dimensional images. This paper applies deep convolutional neural networks to classify hyperspectral images directly in the spectral domain. The proposed classifier uses a five‑layer network—input, convolution, max‑pooling, fully connected, and output—applied to each spectral signature. Experiments on several hyperspectral datasets show that the method outperforms traditional approaches such as support vector machines and other deep learning baselines.

Abstract

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.