Remote Sensing Applications and Decision Support

Band selection algorithm based on information entropy for hyperspectral image classification

[+] Author Affiliations
Li Xie, Guangyao Li, Lei Peng, Qiaochuan Chen

Tongji University, College of Electronics and Information Engineering, Shanghai, China

Yunlan Tan

National Administration of Surveying, Mapping and Geoinformation, Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, Ji’an, Jiangxi, China

Jinggangshan University, School of Electronic Information and Engineering, Ji’an, Jiangxi, China

Mang Xiao

Shanghai Institute of Technology, School of Computer Science and Information Engineering, Shanghai, China

J. Appl. Remote Sens. 11(2), 026018 (May 22, 2017). doi:10.1117/1.JRS.11.026018
History: Received November 16, 2016; Accepted April 28, 2017
Text Size: A A A

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.

Figures in this Article
© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Li Xie ; Guangyao Li ; Lei Peng ; Qiaochuan Chen ; Yunlan Tan, et al.
"Band selection algorithm based on information entropy for hyperspectral image classification", J. Appl. Remote Sens. 11(2), 026018 (May 22, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.026018


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.