Regular Articles

Hyperspectral image filtering with adaptive manifold for classification

[+] Author Affiliations
Weiying Xie, Yunsong Li

Xidian University, State Key Laboratory of Integrated Service Network, Xi’an, China

Weiping Zhou

Air Force Xi’an Flight Academy, Xi’an, China

J. Electron. Imaging. 26(3), 033025 (Jun 23, 2017). doi:10.1117/1.JEI.26.3.033025
History: Received February 20, 2017; Accepted June 6, 2017
Text Size: A A A

Abstract.  Hyperspectral image (HSI) is a three-dimensional data cube containing two spatial information dimensions and one spectral information dimension. The spectral vectors of different classes may have similar tendency and value that may bring about negative influences on classification. It is, therefore, important to introduce signal preprocessing techniques in the spatial domain to improve classification accuracy of HSIs. Assuming that local pixels in HSI have some correlations with each other, this paper proposes a spatial filtering model based on adaptive manifold (AM) for HSI. The AM for spatial filtering emphasizes the similar neighboring pixels and is robust to resist the noisy points with fast speed. The rich information in the filtered data is effective for improving the performance of the subsequent classification. The filtered data are classified by an extreme learning machine (ELM). The experimental results indicate that the framework built based on AM and ELM provides competitive performance. Specifically, by classifying the filtered data, the average accuracy of ELM can be improved as high as 30.54%, while performing tens to hundreds times faster than those state-of-the-art classifiers.

© 2017 SPIE and IS&T

Citation

Weiying Xie ; Yunsong Li and Weiping Zhou
"Hyperspectral image filtering with adaptive manifold for classification", J. Electron. Imaging. 26(3), 033025 (Jun 23, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.3.033025


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.