Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches

95 Pages·2021·43.778 MB·English

by  Arian Azarang, Nasser Kehtarnavaz

Series ISSN: 1559-8136
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Synthesis Lectures on A
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Image, Video & Multimedia Processing G
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Series Editor: Alan C. Bovik, University of Texas, Austin A
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Image Fusion in
Image Fusion in Remote Sensing
Conventional and Deep Learning Approaches
Arian Azarang, The University of Texas at Dallas Remote Sensing

Nasser Kehtarnavaz, The University of Texas at Dallas
I(mmuaglteis fpuesciotrna li)n i mremagoetse t sheants ainreg coarp ptuanresdh abryp deniffinegre inntv soelnvesso rfus soinn gs astpealtliitael s(.p Thanicsh brooomka atidcd) raenssde ssp iemctargael I Conventional and Deep
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fusion approaches for remote sensing applications. Both conventional and deep learning approaches A
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are covered. First, the conventional approaches to image fusion in remote sensing are discussed. E
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These approaches include component substitution, multi-resolution, and model-based algorithms. U
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Then, the recently developed deep learning approaches involving single-objective and multi-objective O
N Learning Approaches
loss functions are discussed. Experimental results are provided comparing conventional and deep IN
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learning approaches in terms of both low-resolution and full-resolution objective metrics that are E
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commonly used in remote sensing. The book is concluded by stating anticipated future trends in O
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pansharpening or image fusion in remote sensing. E
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Arian Azarang

Nasser Kehtarnavaz
About SYNTHESIS

This volume is a printed version of a work that appears in the Synthesis
Digital Library of Engineering and Computer Science. Synthesis
books provide concise, original presentations of important research and M
O
development topics, published quickly, in digital and print formats.
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store.morganclaypool.com P
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Image Fusion in Remote Sensing
Conventional and Deep Learning Approaches

Synthesis Lectures on Image,
Video, and Multimedia
Processing
Editor
AlanC.Bovik,UniversityofTexas,Austin
TheLecturesonImage,VideoandMultimediaProcessingareintendedtoprovideauniqueand
groundbreakingforumfortheworld’sexpertsinthefieldtoexpresstheirknowledgeinuniqueand
effectiveways.ItisourintentionthattheSerieswillcontainLecturesofbasic,intermediate,and
advancedmaterialdependingonthetopicalmatterandtheauthors’levelofdiscourse.Itisalso
intendedthattheseLecturesdepartfromtheusualdrytextbookformatandinsteadgivetheauthor
theopportunitytospeakmoredirectlytothereader,andtounfoldthesubjectmatterfromamore
personalpointofview.Thesuccessofthiscandidapproachtotechnicalwritingwillrestonour
selectionofexceptionallydistinguishedauthors,whohavebeenchosenfortheirnoteworthy
leadershipindevelopingnewideasinimage,video,andmultimediaprocessingresearch,
development,andeducation.
Intermsofthesubjectmatterfortheseries,therearefewlimitationsthatwewillimposeother
thantheLecturesberelatedtoaspectsoftheimagingsciencesthatarerelevanttofurtheringour
understandingoftheprocessesbywhichimages,videos,andmultimediasignalsareformed,
processedforvarioustasks,andperceivedbyhumanviewers.Thesecategoriesarenaturallyquite
broad,fortworeasons:First,measuring,processing,andunderstandingperceptualsignalsinvolves
broadcategoriesofscientificinquiry,includingoptics,surfacephysics,visualpsychophysicsand
neurophysiology,informationtheory,computergraphics,displayandprintingtechnology,artificial
intelligence,neuralnetworks,harmonicanalysis,andsoon.Secondly,thedomainofapplicationof
thesemethodsislimitedonlybythenumberofbranchesofscience,engineering,andindustrythat
utilizeaudio,visual,andotherperceptualsignalstoconveyinformation.Weanticipatethatthe
Lecturesinthisserieswilldramaticallyinfluencefuturethoughtonthesesubjectsasthe
Twenty-FirstCenturyunfolds.
ImageFusioninRemoteSensing:ConventionalandDeepLearningApproaches
ArianAzarangandNasserKehtarnavaz
2021
MultimodalLearningTowardMicro-VideoUnderstanding
LigiangNie,MengLiu.andXuemengSong
2019
iv
VirtualRealityandVirtualEnvironmentsin10Lectures
StanislavStanković
2015
DictionaryLearninginVisualComputing
QiangZhangandBaoxinLi
2015
CombatingBadWeatherPartII:FogRemovalfromImageandVideo
SudiptaMukhopadhyayandAbhishekKumarTripathi
2015
CombatingBadWeatherPartI:RainRemovalfromVideo
SudiptaMukhopadhyayandAbhishekKumarTripathi
2014
ImageUnderstandingUsingSparseRepresentations
JayaramanJ.Thiagarajan,KarthikeyanNatesanRamamurthy,PavanTuraga,andAndreasSpanias
2014
ContextualAnalysisofVideos
MyoThida,How-lungEng,DorothyMonekosso,andPaoloRemagnino
2013
WaveletImageCompression
WilliamA.Pearlman
2013
RemoteSensingImageProcessing
GustavoCamps-Valls,DevisTuia,LuisGómez-Chova,SandraJiménez,andJesúsMalo
2011
TheStructureandPropertiesofColorSpacesandtheRepresentationofColorImages
EricDubois
2009
BiomedicalImageAnalysis:Segmentation
ScottT.ActonandNilanjanRay
2009
JointSource-ChannelVideoTransmission
FanZhaiandAggelosKatsaggelos
2007
SuperResolutionofImagesandVideo
AggelosK.Katsaggelos,RafaelMolina,andJavierMateos
2007
v
TensorVoting:APerceptualOrganizationApproachtoComputerVisionandMachine
Learning
PhilipposMordohaiandGérardMedioni
2006
LightFieldSampling
ChaZhangandTsuhanChen
2006
Real-TimeImageandVideoProcessing:FromResearchtoReality
NasserKehtarnavazandMarkGamadia
2006
MPEG-4BeyondConventionalVideoCoding:ObjectCoding,Resilience,and
Scalability
MihaelavanderSchaar,DeepakSTuraga,andThomasStockhammer
2006
ModernImageQualityAssessment
ZhouWangandAlanC.Bovik
2006
BiomedicalImageAnalysis:Tracking
ScottT.ActonandNilanjanRay
2006
RecognitionofHumansandTheirActivitiesUsingVideo
RamaChellappa,AmitK.Roy-Chowdhury,andS.KevinZhou
2005
Copyright©2021byMorgan&Claypool
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin
anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations
inprintedreviews,withoutthepriorpermissionofthepublisher.
ImageFusioninRemoteSensing:ConventionalandDeepLearningApproaches
ArianAzarangandNasserKehtarnavaz
www.morganclaypool.com
ISBN:9781636390741 paperback
ISBN:9781636390758 ebook
ISBN:9781636390765 hardcover
DOI10.2200/S01074ED1V01Y202101IVM021
APublicationintheMorgan&ClaypoolPublishersseries
SYNTHESISLECTURESONIMAGE,VIDEO,ANDMULTIMEDIAPROCESSING
Lecture#13
SeriesEditor:AlanC.Bovik,UniversityofTexas,Austin
SeriesISSN
Print1559-8136 Electronic1559-8144
Image Fusion in Remote Sensing
Conventional and Deep Learning Approaches
Arian Azarang and Nasser Kehtarnavaz
TheUniversityofTexasatDallas
SYNTHESISLECTURESONIMAGE,VIDEO,ANDMULTIMEDIA
PROCESSING#13
M
&C Morgan &cLaypool publishers
ABSTRACT
Image fusion in remote sensing or pansharpening involves fusing spatial (panchromatic) and
spectral (multispectral) images that are captured by different sensors on satellites. This book
addresses image fusion approaches for remote sensing applications. Both conventional and
deeplearningapproachesarecovered.First,theconventionalapproachestoimagefusioninre-
motesensingarediscussed.Theseapproachesincludecomponentsubstitution,multi-resolution,
andmodel-basedalgorithms.Then,therecentlydevelopeddeeplearningapproachesinvolving
single-objective and multi-objective loss functions are discussed. Experimental results are pro-
videdcomparingconventionalanddeeplearningapproachesintermsofbothlow-resolutionand
full-resolution objective metrics that are commonly used in remote sensing. The book is con-
cludedbystatinganticipatedfuturetrendsinpansharpeningorimagefusioninremotesensing.
KEYWORDS
imagefusioninremotesensing,pansharpening,deeplearning-basedimagefusion,
fusionofspatialandspectralsatelliteimages