Comments on "PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling With Masked Networks"

被引:2
作者
Denis, Loic [1 ,2 ]
机构
[1] Univ Jean Monnet St Etienne, Inst dOpt Grad Sch, CNRS, Lab Hubert Curien,UMR 5516, F-42023 St Etienne, France
[2] Inst Polytech Paris, LTCI, Telecom Paris, Palaiseau, France
关键词
Despeckling; multivariate Gaussian models; polarimetric SAR (PolSAR); self-supervised learning;
D O I
10.1109/LGRS.2024.3387994
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
A self-supervised despeckling approach based on the decomposition of single-look complex (SLC) synthetic aperture radar (SAR) images into their real and imaginary components has recently been introduced under the name MERLIN. At its core is the observation that, under Goodman's fully developed speckle model, the real and imaginary parts of single-channel SAR images are independent and identically distributed (i.i.d.). This letter commented here, PolMERLIN, proposes an extension to multichannel SAR images, such as polarimetric SAR (PolSAR) images. This extension is based on the independence between real and imaginary parts of PolSAR images, but this independence generally does not hold, as shown here.
引用
收藏
页码:1 / 1
页数:2
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