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Enhancement of Class Separability for Polarimetric TerraSAR-X Data and Its Application to Crop Classification in Leizhou Peninsula, Southern China

  • Hongzhong LiEmail author
  • Yu Han
  • Jinsong Chen
  • Shanxin Guo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

In this paper, an enhanced class separability is proposed for multi-look fully polarimetric SAR classification. Instead of measuring the Wishart distance between two classes directly, we apply the deorientation procedure to eliminate the fluctuation of polarization orientation angle, which is an extrinsic property of targets and might result in larger inner class distance. Then the Barnes-Holm decomposition is used to factorize the deorientationed coherency matrix into a pure target and a distributed target, and the enhanced class separability, which is proved strictly, is measured by the sum of two Wishart distances based on pure targets and distributed targets respectively. The effectiveness of the proposed measure is demonstrated with the TerraSAR X-band PolSAR data in crop classification, in Leizhou Peninsula, southern China.

Keywords

Class separability Crop classification Wishart distance Barnes-Holm decomposition 

Notes

Acknowledgments

The work was funded by the Fundamental Research Foundation of Shenzhen Technology and Innovation Council (JCYJ20170818155853672, JCYJ20160429191127529), Natural science foundation of China project 41771403, research project from the Chinese Academy of Sciences (XDA05050107-03, XDA19030301), and the Agricultural Scientific Research Outstanding Talent Fund, Agricultural Information Technology Key Laboratory Opening Fund of Ministry of Agriculture (2016006). We wish to take this opportunity to express their sincere acknowledgment to them.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hongzhong Li
    • 1
    • 2
    Email author
  • Yu Han
    • 1
  • Jinsong Chen
    • 1
  • Shanxin Guo
    • 1
  1. 1.Shenzhen Institute of Advanced Technology, CASShenzhenPeople’s Republic of China
  2. 2.Key Laboratory of Agricultural Remote SensingMinistry of Agriculture and Rural AffairsBeijingPeople’s Republic of China

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