Subspace matching pursuit with Dice coefficient for sparse unmixing of hyperspectral data

Abstract

Sparse unmixing is a popular linear spectral unmixing tool in remotely sensed hyperspectral data interpretation. It can be worked out in semisupervised fashion by taking the advantage of the spectral library known in advance. Most sparse regressions methods are based on convex relaxation methods which try to obtain the global solution of a well-defined… (More)
DOI: 10.1109/IGARSS.2016.7730720

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Cite this paper

@article{Li2016SubspaceMP, title={Subspace matching pursuit with Dice coefficient for sparse unmixing of hyperspectral data}, author={Dan Li and Chunmei Zhang and Qianqi Zhou and Junyan Wang and Guodong Xu}, journal={2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, year={2016}, pages={6585-6588} }