Global Overcomplete Dictionary-Based Sparse and Nonnegative Collaborative Representation for Hyperspectral Target Detection (GODSNCR), IEEE TGRS, 2024.

This is the initial version of the guideline and will be updated

1. Run HDBSCAN.py to get the purified and unpurified background dictionaries
2. Run GODSNCR.m to get the final detection result.

REFERENCE DATA: AVIRIS_100_100_189.mat

Cite

@ARTICLE{10478971,
  author={Li, Chenxing and Zhu, Dehui and Wu, Chen and Du, Bo and Zhang, Liangpei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Global Overcomplete Dictionary-Based Sparse and Nonnegative Collaborative Representation for Hyperspectral Target Detection}, 
  year={2024},
  volume={62},
  number={},
  pages={1-14},
  keywords={Dictionaries;Hyperspectral imaging;Object detection;Collaboration;Detectors;Sparse approximation;Feature extraction;Collaborative representation;hyperspectral imagery (HSI);sparse representation;target detection},
  doi={10.1109/TGRS.2024.3381719}}