This is the code for our paper published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Title of the paper: Active Learning-based Spectral-Spatial Classification for Discriminating Tree Species in Hyperspectral Images
Authors: Fei Tong and Yun Zhang
To set up the python environment, required libraries are:
numpy
scipy
numba
matplotlib
gdal
imageio
scikit-learn
joblib
opencv-python
Before running the code, please download the dataset and put all downloaded files in the folder "data\images"
For example, if you want to run ALSSC_PaviaU_Demo.py, please download and put both "PaviaU.mat" and "PaviaU_gt.mat" in the folder "data\images\PaviaU"
For Pavia University and Salinas datasets, you can download in this website:https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.
For Tree Species dataset, you can download here: https://github.com/Bin-Zh/Three-dimensional-convolutional-neural-network-model-for-tree-species-classification-using-airborne-/tree/master/data/testarea2_new
If you have any question, welcome to contact me (ftong@unb.ca).