@inproceedings{f37557e6a3304feaa50eaece27f0d872,
title = "Hyperspectral image band selection via global optimal clustering",
abstract = "Band selection, by choosing a set of representative bands in hyperspectral images (HSI), is concerned to be an effective method to eliminate the 'Hughes phenomenon'. In this paper, we present a global optimal clustering-based band selection (GOC) algorithm based on the hypothesis that all the bands in a cluster are continuous at their wavelengths. After the clustering result is obtained, we propose a greedy-based method to select representative bands in each cluster, trying to minimize the linear reconstruction error. Experiment on a real HSI dataset shows that the proposed method outperforms the state-of-the-art competitors.",
keywords = "Band selection, dynamic programming, global optimal, hyperspectral image",
author = "Fahong Zhang and Qi Wang and Xuelong Li",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8126818",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
}