Journal Article

Distance Measures of Polarimetric SAR Image Data: A Survey

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

2022

Overview

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.

• Publisher: MDPI AG
• Subject: Algorithms / Clustering / geodesic distances / GLRT distances / Hypothesis testing / Image processing / Likelihood ratio / maximum likelihood distances / norm distances / Normal distribution / Polarimetry / PolSAR image / Radar imaging / stochastic distances / Surveys / Synthetic aperture radar