IEEE Geoscience and Remote Sensing Magazine - March 2021 - 82

Synthetic Aperture
Radar Image
Statistical Modeling
Part One-Single-pixel statistical models
©SHUTTERSTOCK.COM/VAALAA

DONG-XIAO YUE, FENG XU, ALEJANDRO C. FRERY, AND YA-QIU JIN

W

ith the rapid development of spaceborne synthetic
aperture radar (SAR) technology and the acquisition
of a large volume of SAR images, SAR image interpretation
has become an urgent and difficult research topic. SAR
image statistical modeling is one of the theoretical foundations for SAR image interpretation. It is of great value for
the in-depth analysis of SAR images. This article reviews
the major developments of SAR image statistical modeling since its beginning, including more than 20 statistical
distributions of eight statistical models, and gives their
derivations and expressions, which can be used as a basic
reference for the statistical modeling of SAR images (see
" Notations and Definitions " ).
Digital Object Identifier 10.1109/MGRS.2020.3004508
Date of current version: 13 August 2020

82

0274-6638/21©2021IEEE

INTRODUCTION
BACKGROUND
SAR has become an important tool for land survey, resource mapping, environment monitoring, disaster rescue, and national security due to its all-time, all-weather,
high-penetration, and high-resolution imaging capability
on a global scale. SAR systems have evolved from low to
high resolution, single polarization (single-pol) to full
polarization (full-pol), single temporal to multitemporal, and single incidence to multiple incidences, among
other features. The huge volume of archived SAR images
increases by new observations on a daily basis. Computerized, automated SAR image interpretation has become a
key research direction to enhance the application value of
SAR data [1], [2].
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

MARCH 2021



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IEEE Geoscience and Remote Sensing Magazine - March 2021

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