MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification
It addresses classification challenges in hyperspectral imaging for remote sensing applications, offering an incremental improvement over existing methods.
This paper tackled the problem of low accuracy and noise in hyperspectral image classification by introducing MOB-GCN, a multiscale object-based graph neural network, which outperformed single-scale GCNs in accuracy, efficiency, and noise reduction, especially with limited labeled data.
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