@Article{IJNAM-9-181,
author = {B. Wang, X. Wang and Z. Chen},
title = {Spatial Entropy Based Mutual Information in Hyperspectral Band Selection for Supervised Classification},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2012},
volume = {9},
number = {2},
pages = {181--192},
abstract = {<p style="text-align: justify;">Hyperspectral band image selection is a fundamental problem for hyperspectral remote
sensing data processing. Accepting its importance, several information-based band selection
methods have been proposed, which apply Shannon entropy to measure image information. However,
the Shannon entropy is not accurate in measuring image information since it neglects the
spatial distribution of pixels and is computed only from a histogram. This paper investigates the
potential of spatial entropy in measuring image information and proposes a new mutual information
(MI) band selection method based on the spatial entropy. Then selected band images are
validated for supervised classification via Support Vector Machine (SVM). Using a hyperspectral
AVIRIS 92AV3C dataset, experiment results show that with 20 images selection from 220 bands,
the supervised classification accuracy can reach 90.6%. Comparison with a previous Shannon
entropy-based band selection method shows that the proposed method selects band images which
can achieve more accurate classification results.</p>},
issn = {2617-8710},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/ijnam/619.html}
}