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Title:Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism
Authors:ID Pongrac, Blaž (Author)
ID Gleich, Dušan (Author)
Files:.pdf remotesensing-15-03698-v3_(1).pdf (13,70 MB)
MD5: D965841C3BFB9981C2775A8BC41010B3
 
URL https://www.mdpi.com/2072-4292/15/14/3698
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features.
Keywords:synthetic aperture radar, speckle, speckle suppression, despeckling, deep learning, convolutional neural network
Publication status:Published
Publication version:Version of Record
Submitted for review:25.05.2023
Article acceptance date:20.07.2023
Publication date:24.07.2023
Publisher:MDPI
Year of publishing:2023
Number of pages:Str. 1-25
Numbering:Letn. 15, št. 14, št. članka 3698
PID:20.500.12556/DKUM-86527 New window
UDC:621.39
ISSN on article:2072-4292
COBISS.SI-ID:161242883 New window
DOI:10.3390/rs15143698 New window
Copyright:© 2023 by the authors
Publication date in DKUM:21.02.2024
Views:280
Downloads:29
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
PONGRAC, Blaž and GLEICH, Dušan, 2023, Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism. Remote sensing [online]. 2023. Vol. 15, no. 14,  članka 3698, p. 1–25. [Accessed 27 March 2025]. DOI 10.3390/rs15143698. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=86527
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Record is a part of a journal

Title:Remote sensing
Shortened title:Remote sens.
Publisher:MDPI
ISSN:2072-4292
COBISS.SI-ID:32345133 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0065
Name:Telematika

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:24.07.2023

Secondary language

Language:Slovenian
Abstract:V tem članku smo predstavilo odstranjevanje pegastega šuma slik s sintetično odprtino z uporabo dveh različnih arhitektur konvolucijskih nevronskih mrež. Prva metoda predstavlja novo siamsko konvolucijsko nevronsko mrežo z razširjeno konvolucijsko mrežo v vsaki veji. Nedavno so bili v konvolucijska omrežja uvedeni mehanizmi pozornosti za boljše modeliranje in prepoznavanje funkcij. Zato predlagamo novo zasnovo za konvolucijsko nevronsko mrežo z uporabo mehanizma pozornosti za omrežje tipa kodirnik dekodirnik. Ogrodje je sestavljeno iz večstopenjskega omrežja prostorske pozornosti za izboljšanje modeliranja semantičnih informacij na različnih prostorskih ravneh in dodatnega mehanizma pozornosti za optimizacijo širjenja značilnosti. Obe predlagani metodi se razlikujeta po zasnovi, vendar zagotavljata primerljive rezultate odstranjevanja pegastega šuma pri subjektivnih in objektivnih meritvah v smislu koreliranega pegastega šuma. Eksperimentalni rezultati so ovrednoteni tako na sintetično ustvarjenih pegastih slikah kot na resničnih slikah SAR. Metode, predlagane v tem dokumentu, lahko odstranijo pegasti šum iz slik SAR in ohranijo bistvene lastnosti slik SAR.
Keywords:radarji, globoko učenje, konvolucijske nevronske mreže


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