Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2501.04643v1
• Date: Wed, 08 Jan 2025 17:49:52 GMT
• Title: Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification
• Authors: Zhiqiang Gao, Jiaqi Wang, Hangchi Shen, Zhihao Dou, Xiangbo Zhang, Kaizhu Huang,
• Abstract summary: Hyperspectral image (HSI) classification is a crucial technique for remote sensing to build large-scale earth monitoring systems. One recent feasible solution for HSI is to leverage CapsNets for capturing spectral-spatial information. We propose a DWT-CapsNet to identify partial but important connections in CapsNet for an effective and efficient HSI classification.
• Score: 32.54546441663001
• License:
• 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.

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