Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification

TL;DR

This paper introduces a DWT-based capsule network with attention and multi-scale routing for hyperspectral image classification, achieving high accuracy with reduced computational costs.

Contribution

It proposes a novel DWT-CapsNet architecture with attention mechanisms and a multi-scale routing algorithm to improve efficiency and accuracy in hyperspectral image classification.

Findings

Achieves state-of-the-art accuracy on HSI datasets.

Reduces computational demands compared to traditional CapsNet.

Effectively captures spectral-spatial information with fewer parameters.

Abstract

Hyperspectral image (HSI) classification is a crucial technique for remote sensing to build large-scale earth monitoring systems. HSI contains much more information than traditional visual images for identifying the categories of land covers. One recent feasible solution for HSI is to leverage CapsNets for capturing spectral-spatial information. However, these methods require high computational requirements due to the full connection architecture between stacked capsule layers. To solve this problem, a DWT-CapsNet is proposed to identify partial but important connections in CapsNet for a effective and efficient HSI classification. Specifically, we integrate a tailored attention mechanism into a Discrete Wavelet Transform (DWT)-based downsampling layer, alleviating the information loss problem of conventional downsampling operation in feature extractors. Moreover, we propose a novel multi-scale routing algorithm that prunes a large proportion of connections in CapsNet. A capsule pyramid fusion mechanism is designed to aggregate the spectral-spatial relationships in multiple levels of granularity, and then a self-attention mechanism is further conducted in a partially and locally connected architecture to emphasize the meaningful relationships. As shown in the experimental results, our method achieves state-of-the-art accuracy while keeping lower computational demand regarding running time, flops, and the number of parameters, rendering it an appealing choice for practical implementation in HSI classification.