Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image Classification

This is an implementation of "Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image Classification" that is accepted for Publiaction in IEEE-WHISPERS 2023

Datasets

In our experiments, two of the most commonly used HSI datasets are adopted, namely, Pavia University and Salinas. The Pavia University and Salinas datasets can be collected from https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

Requirements

python 3.9, Tensorflow 2.10.0, Spyder IDE

Results

To quantitatively measure the proposed model, three evaluation metrics are employed to verify the effectiveness of the algorithm, including Overall Accuracy (OA), Average Accuracy (AA) and Cohen's Kappa (k).

Model was qualitatively evaluated by visually comparing the resulting class maps.

Feel Free to contact me on: mqalkhatib@ieee.org