Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition
run "main_detect_syn.m" If you are using your own data, please uncomment the following codes.
% % spectral unmixing % tic % [Aest,sest] = do_nmfdecomp(input,numComp,M,N); % toc
Ying Qu ([email protected]), EECS, University of Tennessee, Knoxville
If you find the code helpful, please kindly cite the following paper.
Ying. Qu and Wei. Wang and Rui. Guo and Bulent. Ayhan and Chiman. Kwan and Steven. Vance and Hairong. Qi, "Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4391-4405, Aug. 2018.
@ARTICLE{ADLR_TGRS, author={Ying. Qu and Wei. Wang and Rui. Guo and Bulent. Ayhan and Chiman. Kwan and Steven. Vance and Hairong. Qi}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition}, year={2018}, volume={56}, number={8}, pages={4391-4405}, month={Aug},}
Y. Qu et al., "Anomaly detection in hyperspectral images through spectral unmixing and low rank decomposition," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016, pp. 1855-1858.
@INPROCEEDINGS{ADLR_IGARSS, author={Ying. Qu and Rui. Guo and Wei. Wang and Hairong. Qi and Bulent. Ayhan and Chiman. Kwan and Steven. Vance}, booktitle={2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, title={Anomaly detection in hyperspectral images through spectral unmixing and low rank decomposition}, year={2016}, volume={}, number={}, pages={1855-1858}, month={July},}
The paper received the Best Student Paper Award in 2016 from the IEEE Geoscience and Remote Sensing Society.