@inproceedings{9c03feaeec464979ae4d426e53f1a123,
title = "Decision fusion for hyperspectral image classification based on minimum-distance classifiers in thewavelet domain",
abstract = "A decision-fusion approach is introduced for hyperspectral data classification based on minimum-distance classifiers in the wavelet domain. In the proposed approach, multi-scale features of each hyperspectral pixel are extracted by implementing a redundant discrete wavelet transformation on the spectral signature. Following this, a pair of minimumdistance classifiers - a local mean-based nonparametric classifirer and a nearest regularization subspace - are applied on wavelet coefficients at each scale. Classification results are finally merged in a multi-classifier decision-fusion system. Experimental results using real hyperspectral data demonstrate the benefits of the proposed approach - in addition to improved classification performance compared to a traditional single classifier, the resulting classifier framework is effective even for low signal-to-noise-ratio images.",
keywords = "decision fusion, hyperspectral data, nearest neighbors, pattern classification",
author = "Wei Li and Saurabh Prasad and Tramel, \{Eric W.\} and Fowler, \{James E.\} and Qian Du",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 ; Conference date: 09-07-2014 Through 13-07-2014",
year = "2014",
month = sep,
day = "3",
doi = "10.1109/ChinaSIP.2014.6889223",
language = "English",
series = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "162--165",
booktitle = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",
address = "United States",
}