Multi-Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification

Abstract

Aiming at the problem of image information loss, dilated convolution is introduced and a novel multi-scale dilated convolutional neural network (MDCNN) is proposed. Dilated convolution can polymerize image multi-scale information without reducing the resolution. The first layer of the network used spectral convolutional step to reduce dimensionality. Then the multi-scale aggregation extracted multi-scale features through applying dilated convolution and shortcut connection. The extracted features which represent properties of data were fed through Softmax to predict the samples. MDCNN achieved the overall accuracy of 99.58% and 99.92% on two public datasets, Indian Pines and Pavia University. Compared with four other existing models, the results illustrate that MDCNN can extract better discriminative features and achieve higher classification performance.