# Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1905.06133 · HTML: https://mlsanity.com/paper/1905.06133/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 30 of 31)

- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information](https://mlsanity.com/paper/2204.09860/) (2022)
- [A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples](https://mlsanity.com/paper/2112.01800/) (2021)
- [A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2102.08041/) (2021)
- [DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2304.09915/) (2023)
- [Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion](https://mlsanity.com/paper/2108.06073/) (2021)
- [SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks](https://mlsanity.com/paper/2112.11628/) (2021)
- [GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.08255/) (2024)
- [Hyperspectral Image Classification With Contrastive Graph Convolutional Network](https://mlsanity.com/paper/2205.11237/) (2022)
- [Survey of Image Based Graph Neural Networks](https://mlsanity.com/paper/2106.06307/) (2021)
- [DWFormer: Dynamic Window transFormer for Speech Emotion Recognition](https://mlsanity.com/paper/2303.01694/) (2023)
- [Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification](https://mlsanity.com/paper/2106.13952/) (2021)
- [Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2412.03893/) (2024)
- [Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.05823/) (2022)
- [Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation](https://mlsanity.com/paper/2411.03223/) (2024)
- [Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data](https://mlsanity.com/paper/2104.13268/) (2021)
- [MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification](https://mlsanity.com/paper/2110.03346/) (2021)
- [Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks](https://mlsanity.com/paper/2111.15018/) (2021)
- [Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection](https://mlsanity.com/paper/2303.14322/) (2023)
- [RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Imagesbased Semantic Segmentation](https://mlsanity.com/paper/2109.09148/) (2021)
- [Filtration Surfaces for Dynamic Graph Classification](https://mlsanity.com/paper/2309.03616/) (2023)
- [Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks](https://mlsanity.com/paper/2311.11782/) (2023)
- [Adaptive Convolutional Neural Network for Image Super-resolution](https://mlsanity.com/paper/2402.15704/) (2024)
- [Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution](https://mlsanity.com/paper/2106.14804/) (2021)
- [Classification of Hyperspectral Images by Using Spectral Data and Fully Connected Neural Network](https://mlsanity.com/paper/2201.02821/) (2022)
- [A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images](https://mlsanity.com/paper/2202.13275/) (2022)
- [Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification](https://mlsanity.com/paper/2211.07316/) (2022)
- [MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2310.18550/) (2023)
- [S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images](https://mlsanity.com/paper/2404.00964/) (2024)
- [Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis](https://mlsanity.com/paper/2407.15321/) (2024)

## Abstract (arXiv)

Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information. Different from the commonly used GCN models which work on a fixed graph, we enable the graph to be dynamically updated along with the graph convolution process, so that these two steps can be benefited from each other to gradually produce the discriminative embedded features as well as a refined graph. Moreover, to comprehensively deploy the multi-scale information inherited by hyperspectral images, we establish multiple input graphs with different neighborhood scales to extensively exploit the diversified spectral-spatial correlations at multiple scales. Therefore, our method is termed 'Multi-scale Dynamic Graph Convolutional Network' (MDGCN). The experimental results on three typical benchmark datasets firmly demonstrate the superiority of the proposed MDGCN to other state-of-the-art methods in both qualitative and quantitative aspects.