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

2025-02-22

AI Summary

To address the noise sensitivity of pixel-wise methods and the limitation of single-scale object-based image analysis (OBIA) in capturing multi-level structural information for hyperspectral image (HSI) classification, this paper proposes a dynamic multi-scale graph hierarchical modeling framework. We introduce the first deep integration of multi-scale OBIA with graph neural networks (GNNs), constructing dynamic graph structures via multi-resolution image segmentation and designing a Multiresolution Graph Network (MGN) to jointly learn fine-grained local features and global contextual semantics. Experiments on multiple benchmark HSI datasets demonstrate that our method improves classification accuracy by 3.2–7.8% over single-scale GCNs, maintains robust performance with only 50% labeled samples, and significantly suppresses salt-and-pepper noise. Key contributions include: (i) a dynamic multi-scale graph construction mechanism, (ii) the novel MGN architecture, and (iii) effective multi-level semantic collaborative representation.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Other Foundations of Computer Vision
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

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 overcomes these challenges by extracting and integrating features from multiple segmentation scales, leveraging the Multiresolution Graph Network (MGN) architecture to capture both fine-grained and global spatial patterns. 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

Problem

• Enhance hyperspectral image classification accuracy
• Overcome limitations of single-scale OBIA methods
• Integrate multiscale features using graph neural network

Innovation

• Multiscale object-based graph neural network
• Multiresolution Graph Network architecture
• Dynamic multiscale graph hierarchy