Detecting Changes in Fully Polarimetric SAR Imagery With Statistical Information Theory

Images obtained from coherent illumination processes are contaminated with speckle. A prominent example of such imagery systems is the polarimetric synthetic aperture radar (PolSAR). For such a remote sensing tool, the speckle interference pattern appears in the form of a positive-definite Hermitian...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2019-03, Vol.57 (3), p.1380-1392
Main Authors: Nascimento, Abraao D. C., Frery, Alejandro C., Cintra, Renato J.
Format: Article
Language:English
Subjects:
Backscattering
Change detection
Computer simulation
contrast
Covariance matrices
Covariance matrix
Data
Data models
Detection
Distance
Distribution
Entropy
hypothesis test
Imagery
Information theory
Likelihood ratio
Mathematical models
Matrix methods
Maximum likelihood estimation
Parameters
Radar imaging
Radar polarimetry
Remote sensing
SAR (radar)
Speckle
Statistical methods
Statistics
Synthetic aperture radar
Tests
Wishart
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Images obtained from coherent illumination processes are contaminated with speckle. A prominent example of such imagery systems is the polarimetric synthetic aperture radar (PolSAR). For such a remote sensing tool, the speckle interference pattern appears in the form of a positive-definite Hermitian matrix, which requires specialized models and makes change detection a hard task. The scaled complex Wishart distribution is a widely used model for PolSAR images. Such a distribution is defined by two parameters: the number of looks and the complex covariance matrix. The last parameter contains all the necessary information to characterize the backscattered data, and thus, identifying changes in a sequence of images can be formulated as a problem of verifying whether the complex covariance matrices differ at two or more takes. This paper proposes a comparison between a classical change detection method based on the likelihood ratio and three statistical methods that depend on information-theoretic measures: the Kullback-Leibler (KL) distance and two entropies. The performance of these four tests was quantified in terms of their sample test powers and sizes using simulated data. The tests are then applied to actual PolSAR data. The results provide evidence that tests based on entropies may outperform those based on the KL distance and likelihood ratio statistics.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2018.2866367