Efficient global context graph convolution for hyperspectral image classification ## IGARSS-2022

Introduction

This work focuses on integrating long-range contextual information for HSI classification. Concretely, the efficient graph learning module is embedded in the FCN as a learning unit to capture the long-range contextual information and aggregate features to infer underlying semantic relationships with computation efficiency.

Requirements

• Ununtu 16.0
• python 3.7
• Pytorch 1.4
• pytorch-geometric

Training and Testing

put HSI dataset in Datasets folder
run the Demo_IP.py for Indian Pines dataset training and testing