Distance Measures of Polarimetric SAR Image Data: A Survey

Xianxiang Qin, Yanning Zhang, Ying Li, Yinglei Cheng, Wangsheng Yu, Peng Wang, Huanxin Zou

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations

Abstract

Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields.

Original languageEnglish
Article number5873
JournalRemote Sensing
Volume14
Issue number22
DOIs
StatePublished - Nov 2022

Keywords

  • geodesic distances
  • GLRT distances
  • inter-patch distances
  • maximum likelihood distances
  • metric learning
  • norm distances
  • PolSAR image
  • stochastic distances

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