Band selection algorithm based on information entropy for hyperspectral image classification

Li Xie; Guangyao Li; Lei Peng; Qiaochuan Chen; Yunlan Tan; Mang Xiao

doi:10.1117/1.JRS.11.026018

22 May 2017 Band selection algorithm based on information entropy for hyperspectral image classification

Li Xie, Guangyao Li, Lei Peng, Qiaochuan Chen, Yunlan Tan, Mang Xiao

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 026018 (May 2017). https://doi.org/10.1117/1.JRS.11.026018

Abstract

A band selection algorithm based on information entropy is proposed for hyperspectral image classification. First, original spectral features are transformed into discrete features and represented by a discrete space model. Then, the band selection algorithm based on information entropy is adopted to reduce feature dimensionality. The bands with weak class separability are effectively abandoned by the band selection algorithm. Moreover, support vector machine classifiers with composite kernels are employed to incorporate spatial features into spectral features, reducing speckle errors in the classification maps. The proposed methods are applied to three benchmark hyperspectral data sets for classification. The performance of the proposed methods is compared with a band selection algorithm based on mutual information. The experimental results demonstrate that the band selection algorithm based on information entropy can effectively reduce feature dimensionality and improve classification accuracy.

Citation Download Citation

Li Xie, Guangyao Li, Lei Peng, Qiaochuan Chen, Yunlan Tan, and Mang Xiao "Band selection algorithm based on information entropy for hyperspectral image classification," Journal of Applied Remote Sensing 11(2), 026018 (22 May 2017). https://doi.org/10.1117/1.JRS.11.026018

Received: 16 November 2016; Accepted: 28 April 2017; Published: 22 May 2017

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available