@inproceedings{6962a873f644462ab9f0865be6e6ea3d,
title = "Robust nonlinear unmixing of hyperspectral images with a linear-mixture/nonlinear-fluctuation model",
abstract = "Hyperspectral data unmixing has attracted considerable attention in recent years. Hyperspectral data may however suffer from varying levels of signal-to-noise ratio over spectral bands. In this paper, we investigate a robust approach for nonlinear hyperspectral data unmixing. Each observed pixel is modeled as a linear mixing of endmember spectra with nonlinear fluctuations embedded in a reproducing kernel Hilbert space. Welsch M-estimator is considered for reducing the sensitivity of the unmixing process. Experimental results, with both synthetic and real data, illustrate the effectiveness of the proposed scheme.",
keywords = "Hyperspectral data analysis, M-estimator, nonlinear unmixing, robust unmixing",
author = "Jie Chen and Cedric Richard",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th 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.2016.7730826",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7002--7005",
booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",
}