1D-Convolutional Capsule Network for Hyperspectral Image Classification

TL;DR

This paper introduces a lightweight 1D-convolutional capsule network tailored for hyperspectral image classification, effectively capturing spatial and spectral features with reduced parameters and training effort, outperforming existing methods.

Contribution

The paper proposes a novel 1D-convolutional capsule network that simplifies hyperspectral image classification by reducing complexity and training effort while maintaining high accuracy.

Findings

Outperforms state-of-the-art methods in accuracy

Requires less training effort and fewer parameters

Effective on multiple hyperspectral datasets

Abstract

Recently, convolutional neural networks (CNNs) have achieved excellent performances in many computer vision tasks. Specifically, for hyperspectral images (HSIs) classification, CNNs often require very complex structure due to the high dimension of HSIs. The complex structure of CNNs results in prohibitive training efforts. Moreover, the common situation in HSIs classification task is the lack of labeled samples, which results in accuracy deterioration of CNNs. In this work, we develop an easy-to-implement capsule network to alleviate the aforementioned problems, i.e., 1D-convolution capsule network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial and spectral information on spatial and spectral domains, which is more lightweight than 3D-convolution due to fewer parameters. Secondly, 1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce parameter amount and computational complexity of conventional capsule network. Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is verified by three representative HSI datasets. Experimental results demonstrate that 1D-ConvCapsNet is superior to state-of-the-art methods in both the accuracy and training effort.