Image Restoration for Remote Sensing Overview and toolbox

被引：107

作者：

Rasti, Behnood ^{[1
]}

Chang, Yi ^{[2
]}

Dalsasso, Emanuele ^{[3
]}

Denis, Loic ^{[4
]}

Ghamisi, Pedram ^{[1
,5
]}

机构：

[1] Helmholtz Zentrum Dresden Rossendorf, Helmholtz Inst Freiberg Resource Technol Machine, D-09599 Freiberg, Germany

[2] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Sci & Technol Multispectral Informat Proc Lab, Wuhan 1037, Peoples R China

[3] Telecom Paris, Inst Polytech Paris, Informat Proc & Commun Lab LTCI, F-91120 Palaiseau, France

[4] Univ Lyon, Jean Monnet Univ, Natl Ctr Sci Res, Inst Opt Grad Sch,Hubert Curien Lab, F-42000 St Etienne, France

[5] Inst Adv Res Artificial Intelligence, A-1030 Vienna, Austria

来源：

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE | 2022年 / 10卷 / 02期

关键词：

HYPERSPECTRAL IMAGES; LOW-RANK; SPARSE REPRESENTATION; SPECKLE REDUCTION; NOISE-REDUCTION; STRIPE NOISE; MODIS DATA; SAR; WAVELET; DECONVOLUTION;

D O I：

10.1109/MGRS.2021.3121761

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：

Remote sensing provides valuable information about objects and areas from a distance in either active (e.g., radar and lidar) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering a true unknown image from a degraded observed one. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques along different paths according to sensor type. © 2013 IEEE.

引用

页码：201 / 230

页数：30

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