IEEE Geoscience and Remote Sensing Magazine - March 2021 - 115

Synthetic
Aperture
Radar Image
Statistical Modeling
Part Two-Spatial correlation models
and simulation
DONG-XIAO YUE, FENG XU, ALEJANDRO C. FRERY,
AND YA-QIU JIN

F

ollowing on the first part of our review of synthetic aperture radar (SAR) image statistical modeling [1], which concerns single-pixel statistical models, this article extends our discussion to spatial correlation analysis, focusing on SAR spatial
correlation models and SAR clutter simulation methods. Two
types of spatial correlation models, the product model and the
coherent scatterer model, are summarized: the first considers
correlation characteristics from the image itself while the second analyzes spatial correlation stemming from the underlying scatterer and the physical imaging process. In addition, we
review four spatially correlated clutter simulation methods
based on two classical distributions (K and G 0 ): a product
model-based method, an inverse transform method (ITM)based approach, a coherent scatterer model-based technique,
and the generalized Gaussian coherent scatterer (GGCS) approach. We discuss the advantages and disadvantages of these
models and methods and provide references for further research into the statistical modeling of SAR images.
Digital Object Identifier 10.1109/MGRS.2020.3027609
Date of current version: 17 November 2020

MARCH 2021

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

BACKGROUND
The statistical modeling of SAR images has formed one of
the essential research subjects focused on SAR image interpretation. An in-depth study of the statistical characteristics
of SAR images can provide strong support for SAR image information extraction and interpretation algorithm verification, and it is the theoretical basis for applications such as
target detection and recognition. At present, most statistical
modeling of SAR images considers the single-pixel statistical model, which was summarized from different perspectives in [1]. However, single-pixel models cannot solely represent the texture characteristics of an image, leaving them
far from sufficient for mining SAR image information [2].
Two-pixel statistical models, such as the power spectrum
and the autocorrelation function, can further characterize
the texture information of the spatial variation in SAR imagery, which we call spatially correlated statistical modeling. As
the second part of a treatment of SAR image statistical modeling, this article continues to review the spatial correlation
analysis of SAR images built on the single-pixel statistical
model [1].
	

0274-6638/21©2021IEEE

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IEEE Geoscience and Remote Sensing Magazine - March 2021

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - March 2021

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