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  4. A generalized beta prime distribution as the ratio probability density function for change detection between two SAR intensity images with different number of looks
 
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A generalized beta prime distribution as the ratio probability density function for change detection between two SAR intensity images with different number of looks
File(s)
A_Generalized_Beta_Prime_Distribution_as_the_Ratio_Probability_Density_Function_for_Change_Detection_Between_two_SAR_Intensity_Images_with_Different_Number_of_Looks_accepted_version_.pdf (12.24 MB)
Accepted version
Author(s)
Gallardo i Peres, Gerard
Dall, Jørgen
Mason, Philippa J
Ghail, Richard
Hensley, Scott
Type
Journal Article
Abstract
In the framework of the comparison of synthetic aperture radar (SAR) imagery from the Magellan space mission and the VISAR and VenSAR radar instruments which will be onboard the forthcoming VERITAS and EnVision missions to Venus, the problem of the disparity between the resolutions of the images arises when attempting to define a test statistic with which to detect changes. Reliable change detection requires equivalent spatial resolutions which, for the two different images, inevitably involve different equivalent numbers of looks after speckle-reduction processing. This study presents a method to address this scenario using a generalized beta prime distribution as a probability density function (pdf) which is fit to the histogram of the ratio between the two intensity images. The work demonstrates and verifies the properties of the function, highlights its most useful traits, and elaborates on the mathematical procedure required to achieve a meaningful change detection in line with the classic theory of an equal number of looks. The results show that the method accurately describes the ratio histogram of two SAR-intensity images with different numbers of looks. Furthermore, they demonstrate the adaptability of the method to the presence of high pixel correlation between the images and validate its robustness in the presence of textural complexity when the texture patterns of the images are similar.
Date Issued
2024
Date Acceptance
2024-02-20
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2024, 62
URI
http://hdl.handle.net/10044/1/110071
URL
http://dx.doi.org/10.1109/tgrs.2024.3369509
DOI
https://www.dx.doi.org/10.1109/tgrs.2024.3369509
ISSN
0196-2892
Publisher
Institute of Electrical and Electronics Engineers
Journal / Book Title
IEEE Transactions on Geoscience and Remote Sensing
Volume
62
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
https://creativecommons.org/licenses/by/4.0/
Identifier
http://dx.doi.org/10.1109/tgrs.2024.3369509
Publication Status
Published
Article Number
5206414
Date Publish Online
2024-02-27
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