Research Papers

Binary tree of posterior probability support vector machines for hyperspectral image classification

[+] Author Affiliations
Dongli Wang

Xiangtan University, College of Information Engineering, Xiangtan 411105, China

Donghua University, Glorious Sun School, Shanghai 200051, China

Yan Zhou

Xiangtan University, College of Information Engineering, Xiangtan 411105, China

Jianguo Zheng

Donghua University, Glorious Sun School, Shanghai 200051, China

J. Appl. Remote Sens. 5(1), 053503 (March 11, 2011). doi:10.1117/1.3553800
History: Received January 03, 2010; Revised January 20, 2011; Accepted January 21, 2011; Published March 11, 2011; Online March 11, 2011
Text Size: A A A

The problem of hyperspectral remote sensing images classification is revisited by posterior probability support vector machines (PPSVMs). To address the multiclass classification problem, PPSVMs are extended using binary tree structure and boosting with the Fisher ratio as class separability measure. The class pair with larger Fisher ratio separability measure is separated at upper nodes of the binary tree to optimize the structure of the tree and improve the classification accuracy. Two approaches are proposed to select the class pair and construct the binary tree. One is the so-called some-against-rest binary tree of PPSVMs (SBT), in which some classes are separated from the remaining classes at each node considering the Fisher ratio separability measure. For the other approach, named one-against-rest binary tree of PPSVMs (OBT), only one class is separated from the remaining classes at each node. Both approaches need only to train n – 1 (n is the number of classes) binary PPSVM classifiers, while the average convergence performance of SBT and OBT are O(log2n) and O[(n! − 1)/n], respectively. Experimental results show that both approaches obtain classification accuracy if not higher, at least comparable to other multiclass approaches, while using significantly fewer support vectors and reduced testing time.

Figures in this Article
© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Dongli Wang ; Yan Zhou and Jianguo Zheng
"Binary tree of posterior probability support vector machines for hyperspectral image classification", J. Appl. Remote Sens. 5(1), 053503 (March 11, 2011). ; http://dx.doi.org/10.1117/1.3553800


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.