@inproceedings{42287aa5649b4098b360972e359e00b4,
title = "Collaborative sparse unmixing of hyperspectral data using L2,P norm",
abstract = "Sparse unmixing is a popular method in remotely sensed hyperspectral imagery interpretation. Recently, the collaborative sparse unmixing model has shown its advantage over the traditional single channel sparse unmixing method since it can utilize the subspace nature of the high-dimensional hyperspectral data to alleviate the difficulty caused by the usually high mutual coherence of the spectral library. However, the existing collaborative sparse unmixing model is constructed in the convex ℓ1 norm framework while it is known that using the ℓp (0 < p < 1) norm can find a sparser solution. In this paper, we propose a collaborative sparse unmixing model and a new algorithm based on the ℓ2;p (0 < p < 1) norm. Experimental results on both synthetic and real data demonstrate the effectiveness of the ℓ2;p (0 < p < 1) norm collaborative sparse unmixing model.",
keywords = "Hyperspectral unmixing, collaborative sparse regression, sparse unmixing, ℓ norm",
author = "Dan Wang and Zhenwei Shi and Wei Tang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",
year = "2016",
month = nov,
day = "1",
doi = "10.1109/IGARSS.2016.7730820",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6978--6981",
booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",
address = "美国",
}