Spectral–Spatial Hyperspectral Image Classification Using Dual-Channel Capsule Networks

Abstract

Deep learning methods have shown their marvel performance on hyperspectral image (HSI) classification tasks. In particular, algorithms based on convolution neural network (CNN) outperformed most of the conventional machine learning-based algorithms and have become the mainstream of the current HSI classification research works. Recently, a newly proposed neural network called capsule network (CapsNet) showed its potential to replace the CNNs in various classification tasks with its amazing performance. In this letter, we proposed a new network architecture based on the CapsNet for HSI classification tasks, called dual-channel capsule network (DCCapsNet). Our DCCapsNet model extracts the features from spectral and spatial domains, respectively, with two separate convolution channels and then concatenates and feeds them into the following capsule layers to classify each of the HSI pixels. The model was trained and validated on four real HSI data sets and achieved high accuracy. We also compared our network with some of the state-of-the-art models and found that our model outperformed these competitor models.