@inproceedings{a34ed79c94b947cfbf3e63180e6d23ee,
title = "Locality-preserving nonnegative matrix factorization for hyperspectral image classification",
abstract = "Feature extraction based on nonnegative matrix factorization is considered for hyperspectral image classification. One shortcoming of most remote-sensing data is low spatial resolution, which causes a pixel to be mixed with several pure spectral signatures, or endmembers. To counter this effect, locality-preserving nonnegative matrix factorization is employed in order to extract an endmembers-based feature representation as well as to preserve the intrinsic geometric structure of hyperspectral data. Subsequently, a Gaussian mixture model classifier is employed in the induced-feature subspace. Experimental results demonstrate that the proposed classification system significantly outperforms traditional approaches even in instances of limited training data and severe pixel mixing.",
keywords = "feature extraction, Linear mixing model, nonnegative matrix factorization, pattern classification",
author = "Wei Li and Saurabh Prasad and Fowler, \{James E.\} and Minshan Cui",
year = "2012",
doi = "10.1109/IGARSS.2012.6351273",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1405--1408",
booktitle = "IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
note = "32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 ; Conference date: 22-07-2012 Through 27-07-2012",
}