Dynamic spectral–spatial multiscale feature extraction network for hyperspectral image classification

Abstract

Aiming at the problem of insufficient spectral–spatial feature extraction under the condition of limited labeled samples in hyperspectral image classification task, in this paper, a dynamic spectral–spatial multiscale feature extraction network is proposed to extracting more discriminative feature information. Different from the fixed size kernels to extract single feature information, we add dilated convolution in multiscale convolution network to obtain the fusion of neighborhood and global feature information. Besides, a joint spectral–spatial dynamic convolution network is proposed constructed with double attention branches. Spectral attention module is introduced in dynamic convolution to adaptively enhance the useful bands in classification task. That makes dynamic convolution neural network more effective through reconstructing the feature information obtained from different kernels. The experiments, conducted on two commonly hyperspectral image datasets, demonstrate that the proposed method is superior to other state of art classification methods.