Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization

@article{Miao2007EndmemberEF,
  title={Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization},
  author={Lidan Miao and Hairong Qi},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2007},
  volume={45},
  pages={765-777}
}
Endmember extraction is a process to identify the hidden pure source signals from the mixture. In the past decade, numerous algorithms have been proposed to perform this estimation. One commonly used assumption is the presence of pure pixels in the given image scene, which are detected to serve as endmembers. When such pixels are absent, the image is referred to as the highly mixed data, for which these algorithms at best can only return certain data points that are close to the real endmembers… CONTINUE READING
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