Spectral–spatial classification of hyperspectral remote sensing image based on capsule network

Abstract

Hyperspectral image (HSI) classification is a hot topic in remote sensing community; many researchers have made a great deal of effort in this domain. Recently, deep learning‐based manner paves a new way to better classification accuracy. However, the flow of information between layers and layers (e.g. max‐pooling) in traditional deep architecture turns out to be ineffective. In this study, a novel spectral–spatial classification framework for HSI based on Capsule Network (CapsNet) and dynamic routing algorithm is introduced. The proposed architecture is composed of a hybrid of 1D and 2D convolutional layers and two capsule layers for better and effective mining and combining features. Consequently, experiments on two popular dataset indicate that CapsNet‐based framework outperforms traditional CNN‐based counterparts. Moreover, this study reveals great potential for CapsNet model in the field of HSI classification.