MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2502.16289v2
• Date: Sat, 08 Mar 2025 16:24:32 GMT
• Title: MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification
• Authors: Tuan-Anh Yang, Truong-Son Hy, Phuong D. Dao,
• Abstract summary: This paper introduces a novel multiscale object-based graph neural network called MOB-GCN for hyperspectral image (HSI) classification.<n> Experimental results demonstrate that MOB-GCN consistently outperforms single-scale graph convolutional networks (GCNs) in terms of classification accuracy, computational efficiency, and noise reduction.
• Score: 1.515687944002438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel multiscale object-based graph neural network called MOB-GCN for hyperspectral image (HSI) classification. The central aim of this study is to enhance feature extraction and classification performance by utilizing multiscale object-based image analysis (OBIA). Traditional pixel-based methods often suffer from low accuracy and speckle noise, while single-scale OBIA approaches may overlook crucial information of image objects at different levels of detail. MOB-GCN addresses this issue by extracting and integrating features from multiple segmentation scales to improve classification results using the Multiresolution Graph Network (MGN) architecture that can model fine-grained and global spatial patterns. By constructing a dynamic multiscale graph hierarchy, MOB-GCN offers a more comprehensive understanding of the intricate details and global context of HSIs. Experimental results demonstrate that MOB-GCN consistently outperforms single-scale graph convolutional networks (GCNs) in terms of classification accuracy, computational efficiency, and noise reduction, particularly when labeled data is limited. The implementation of MOB-GCN is publicly available at https://github.com/HySonLab/MultiscaleHSI

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