ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1637820
This article is part of the Research TopicSatellite Remote Sensing for Hydrological and Water Resource Management in Coastal ZonesView all 6 articles
Multi‐Scale Graph Wavelet Convolutional Network for Hyper-spectral Image Classification
Provisionally accepted
- Qiongtai Teachers College, Haikou, China
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Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to the dimensionality curse, making it difficult to describe complex spatial relationships, especially those with non-Euclidean characteristics. This paper presents a multi-scale graph wavelet convolutional network (MS-GWCN) that utilizes a graph wavelet transform within a multi-scale learning framework to accurately capture spatial-spectral features. The MS-GWCN constructs graphs according to 8-neighborhood connectivity schemes, implements spectral graph wavelet transforms for multi-scale decomposition, and aggregates features through multi-scale graph convolutional layers. Our method, the MS-GWCN, demonstrates superior performance compared to existing methodologies. It achieves higher overall accuracy, average accuracy, per-class accuracy, and the Kappa coefficient, as evaluated on three datasets, including the Indian Pines, Salinas, and Pavia University datasets, thereby demonstrating enhanced robustness and generalization capability.
Keywords: Graph wavelet transform, hyperspectral image classification, Spectral-spatial fusion, Multi-scale graph convolutional network, deep learning
Received: 29 May 2025; Accepted: 09 Sep 2025.
Copyright: © 2025 Junhua. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ku Junhua, Qiongtai Teachers College, Haikou, China
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