Multi-Scale Depthwise Separable Convolutional Neural Network for Hyperspectral Image Classification

Hyperspectral images (HSIs) have far more spectral bands than conventional RGB images. The abundant spectral information provides very useful clues for the followup applications, such as classification and anomaly detection. How to extract discriminant features from HSIs is very important. In this work, we propose a novel spatial-spectral features extraction method for HSI classification by Multi-Scale Depthwise Separable Convolutional Neural Network (MDSCNN). This new model consists of a multi-scale atrous convolution module and two bottleneck residual units, which greatly increase the width and depth of the network. In addition, we use depthwise separable convolution instead of traditional 2D or 3D convolution to extract spatial and spectral features. Furthermore, considering classification accuracy can benifit from multi-scale information, we introduce atrous convolution with different dilation rates parallelly to extract more discriminant features of HSIs for classification. Experiments on three standard datasets show that the proposed MDSCNN has got the state-of-the-art accuracy among all compared methods.