A Novel Feature Fusion Approach for VHR Remote Sensing Image Classification

Here, we provide the MATLAB implementation of the paper: A Novel Feature Fusion Approach for VHR Remote Sensing Image Classification

For more ore information, please see our published paper at IEEE JSTARS

Requirement

MATLAB
SVM: RBF

Quick Start

Just run the demo to get started as follows:

TsF_demo.m

After that, you can find the prediction results in Result.

Dataset Preparation

Data structure

"""
Image classification data set with pixel-level binary labels；
├─Image & ImageAP
├─label
├─train_set
└─test_set
"""

License

Code is released for non-commercial and research purposes only. For commercial purposes, please contact the authors.

Citation

If you use this code for your research, please cite our paper:

@ARTICLE{9277624,  
    author={Liu, Sicong and Zheng, Yongjie and Du, Qian and Samat, Alim and Tong, Xiaohua and Dalponte, Michele},  
    journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},   
    title={A Novel Feature Fusion Approach for VHR Remote Sensing Image Classification},   
    year={2021},  
    volume={14},  
    number={},  
    pages={464-473},  
    doi={10.1109/JSTARS.2020.3041868}
}

Acknowledgments

Our code is inspired by MATLAB-EMAP/SVM(RBF), Image Fusion With Guided Filtering (GFF).