多尺度对象图网络提升高光谱图像分类精度与抗噪能力
MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification
- 基于多尺度对象分析构建动态图结构，融合细粒度与全局空间特征
- 在有限标注数据下分类准确率显著优于单尺度GCN，噪声抑制更优
- 适用于小样本高光谱图像分类，尤其适合地物精细识别任务
本文提出一种新型多尺度对象图神经网络MOB-GCN，用于高光谱图像（HSI）分类。传统像素级方法易受斑点噪声影响且精度低，而单尺度对象分析可能遗漏不同层次的图像对象信息。MOB-GCN通过多尺度分割提取并融合特征，利用多分辨率图网络（MGN）架构建模细粒度与全局空间模式。通过构建动态多尺度图层级，实现对高光谱图像细节与上下文的全面理解。实验表明，相较于单尺度图卷积网络（GCN），MOB-GCN在分类精度、计算效率和噪声抑制方面均有显著提升，尤其在标注数据稀缺时表现更优。代码已开源：https://github.com/HySonLab/MultiscaleHSI
原文摘要 · Abstract (English)
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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