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
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.
python 3.9, Tensorflow 2.10.0, Spyder IDE
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