Table Of ContentAdvances in Computer Vision and Pattern Recognition
Saurabh Prasad
Jocelyn Chanussot Editors
Hyperspectral
Image
Analysis
Advances in Machine Learning and
Signal Processing
Advances in Computer Vision and Pattern
Recognition
Founding Editor
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Series Editor
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Saarland, Germany
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More information about this series at http://www.springer.com/series/4205
Saurabh Prasad Jocelyn Chanussot
(cid:129)
Editors
Hyperspectral Image
Analysis
Advances in Machine Learning and Signal
Processing
123
Editors
Saurabh Prasad Jocelyn Chanussot
Department ofElectrical CNRS, Grenoble INP,GIPSA-lab
andComputer Engineering UniversitéGrenoble Alpes
University of Houston Grenoble,France
Houston, TX,USA
ISSN 2191-6586 ISSN 2191-6594 (electronic)
Advances in Computer Vision andPattern Recognition
ISBN978-3-030-38616-0 ISBN978-3-030-38617-7 (eBook)
https://doi.org/10.1007/978-3-030-38617-7
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Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Saurabh Prasad and Jocelyn Chanussot
2 Machine Learning Methods for Spatial and Temporal
Parameter Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Álvaro Moreno-Martínez, María Piles, Jordi Muñoz-Marí,
Manuel Campos-Taberner, Jose E. Adsuara, Anna Mateo,
Adrián Perez-Suay, Francisco Javier García-Haro
and Gustau Camps-Valls
3 Deep Learning for Hyperspectral Image Analysis,
Part I: Theory and Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Sebastian Berisha, Farideh Foroozandeh Shahraki, David Mayerich
and Saurabh Prasad
4 Deep Learning for Hyperspectral Image Analysis,
Part II: Applications to Remote Sensing and Biomedicine . . . . . . . 69
Farideh Foroozandeh Shahraki, Leila Saadatifard, Sebastian Berisha,
Mahsa Lotfollahi, David Mayerich and Saurabh Prasad
5 Advances in Deep Learning for Hyperspectral Image
Analysis—Addressing Challenges Arising in Practical
Imaging Scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Xiong Zhou and Saurabh Prasad
6 Addressing the Inevitable Imprecision: Multiple Instance
Learning for Hyperspectral Image Analysis . . . . . . . . . . . . . . . . . . 141
Changzhe Jiao, Xiaoxiao Du and Alina Zare
7 Supervised, Semi-supervised, and Unsupervised Learning
for Hyperspectral Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Felix M. Riese and Sina Keller
v
vi Contents
8 Sparsity-Based Methods for Classification . . . . . . . . . . . . . . . . . . . 233
Zebin Wu, Yang Xu and Jianjun Liu
9 Multiple Kernel Learning for Hyperspectral
Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Tianzhu Liu and Yanfeng Gu
10 Low Dimensional Manifold Model in Hyperspectral Image
Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
Wei Zhu, Zuoqiang Shi and Stanley Osher
11 Deep Sparse Band Selection for Hyperspectral
Face Recognition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
Fariborz Taherkhani, Jeremy Dawson and Nasser M. Nasrabadi
12 Detection of Large-Scale and Anomalous Changes . . . . . . . . . . . . . 351
Amanda Ziemann and Stefania Matteoli
13 Recent Advances in Hyperspectral Unmixing Using Sparse
Techniques and Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
Shaoquan Zhang, Yuanchao Su, Xiang Xu, Jun Li, Chengzhi Deng
and Antonio Plaza
14 Hyperspectral–Multispectral Image Fusion Enhancement
Based on Deep Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407
Jingxiang Yang, Yong-Qiang Zhao and Jonathan Cheung-Wai Chan
15 Automatic Target Detection for Sparse Hyperspectral Images . . . . 435
Ahmad W. Bitar, Jean-Philippe Ovarlez, Loong-Fah Cheong
and Ali Chehab
Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 463
Chapter 1
Introduction
SaurabhPrasadandJocelynChanussot
Hyperspectral imaging entails acquiring a large number of images over hundreds
(tothousands)ofnarrowbandcontiguouschannels,spanningthevisibleandinfrared
regimesoftheelectromagneticspectrum.Theunderlyingpremiseofsuchimagingis
thatitcapturestheunderlyingprocesses(e.g.,chemicalcharacteristics,biophysical
properties, etc.) at the pixel level. Recent advances in optical sensing technology
(miniaturization and low-cost architectures for spectral imaging) and sensing plat-
formsfromwhichsuchimagerscanbedeployed(e.g.,handhelddevices,unmanned
aerialvehicles)havethepotentialtoenableubiquitousmultispectralandhyperspec-
tral imaging on demand to support a variety of applications, such as biomedicine
andsensingofourenvironment.Inmanyapplications,itispossibletoleveragedata
acquiredbyothermodalities(e.g.,SyntheticApertureRadar,SAR,andLightDetec-
tion and Ranging (LiDAR)) in conjunction with hyperspectral imagery to paint a
completepicture—forexample,hyperspectralimageryandLiDARdatawhenused
together provide information about the underlying chemistry (e.g., as provided by
hyperspectraldata)andtheunderlyingtopography(asprovidedbyLiDARdata)and
canfacilitaterobustland-coverclassification.Althoughthisincreaseinthequality
andquantityofdiversemulti-channelopticaldatacanpotentiallyfacilitateimproved
understandingoffundamentalscientificquestions,thereisastrongneedforrobust
image analysis methods that can address the challenges posed by these imaging
paradigms. While machine learning approaches for image analysis have evolved
B
S.Prasad( )
DepartmentofElectricalandComputerEngineering,UniversityofHouston,
Houston,TX77578,USA
e-mail:[email protected]
J.Chanussot
UniversityofGrenobleAlpes,CNRS,GrenobleINP,GIPSA-lab,
38000Grenoble,France
e-mail:[email protected]
©SpringerNatureSwitzerlandAG2020 1
S.PrasadandJ.Chanussot(eds.),HyperspectralImageAnalysis,
AdvancesinComputerVisionandPatternRecognition,
https://doi.org/10.1007/978-3-030-38617-7_1
2 S.PrasadandJ.Chanussot
to exploit the rich information provided by hyperspectral imagery and other high-
dimensionalimagerydata,keychallengesremainforeffectiveutilizationinanoper-
ationalenvironment,includingthefollowing:
– Representation and effective feature extraction from such high-dimensional
datasets,
– Design of effective learning strategies that are robust to a limited quantity of
trainingsamples(insitudata),missingornoisylabels,andspatial–temporalnon-
stationaryenvironments,
– Designandoptimizationofanalysisalgorithmsthatcaneffectivelyhandlenonlin-
ear,complexdecisionboundariesseparatingclasses(objectsofinterestonground)
inthefeaturespace,
– Theneedtoaddressvariabilityinlightsource→sensor→objectgeometryand
variation in orientation and scale of objects in ubiquitous sensing environments
fromamultitudeofsensorsandsensingplatforms,and
– Effective utilization of the rich and vast quantity of unlabeled data available in
geospatialimageryinconjunctionwithlimitedgroundtruthforrobustanalysis.
This book focuses on advances in machine learning and signal processing for
hyperspectralimageanalysisandpresentsrecentalgorithmicdevelopmentstoward
robust image analysis that address challenges posed by the unique nature of such
imagery. We note that although a majority of the chapters in this book focus on
hyperspectral imagery, these ideas extend to data obtained from other modalities,
suchasmicrowaveremotesensing,multiplexedimmunofluorescenceimaging,etc.
Chaptersinthisbookaregroupedinthechallengestheyaddressbasedonthefol-
lowingbroadthematicareas.
ChallengesinSupervised,Semi-Supervised,andUnsupervisedLearning:Thehigh-
dimensionalnatureofhyperspectralimageryimpliesthatmanylearningalgorithms
thatseektoleveragetheunderlyingspatial–spectralinformationareassociatedwitha
largenumberofdegreesoffreedom,necessitatingarich(inbothqualityandquantity)
representative ground reference data. Leveraging the limited quantity and varying
qualityoflabeleddataassociatedwithremotesensingandbiomedicineapplications
is a critical requirement of successful learning algorithms, and a vast number of
recent developments address this aspect of learning, under the umbrella of super-
vised,semi-supervised,andunsupervisedlearning.Further,theendgoaloflearning
maynotalwaysbediscreteclassification.Numerousapplicationswithhyperspectral
imageryentailmappingspectralobservationstoprediction(e.g.,posedasaregres-
sion problem) of continuous-valued quantities (such as biophysical parameters)—
although there exist commonalities between learning algorithms that are carrying
outdiscreteclassificationandregression,caremustbetakentounderstandtheneeds
andconstraintsofeachapplication.
In Chap.2, Moreno-Martinez et al. survey recent developments in machine
learning for estimating spatial and temporal parameters from multi-channel earth-
observationimages(bothmicrowaveimagingandpassiveopticalimaging).Chap-
ters3 and 4 are a two-part series introducing the foundations of deep learning as
1 Introduction 3
appliedtohyperspectralimageanalysis.InChap.3(PartI),Berishaetal.reviewthe
foundationsofconvolutionalandrecurrentneuralnetworksastheycanbeapplied
forspatial–spectralanalysisofhyperspectralimagery.InChap.4(PartII),Shahraki
etal.presentpracticalarchitecturesanddesignstrategiestosuccessfullydeploysuch
networksforhyperspectralimageanalysistasks.Resultswithhyperspectralimagery
intheareasofremotesensingandbiomedicinearepresented,alongwithadetailed
discussionofthe“successful”networkconfigurationsrelativetothedatacharacter-
istics.InChap.5,ZhouandPrasadreviewrecentdevelopmentsindeeplearningthat
addressthelabelscarcityproblem—includingsemi-supervised,transfer,andactive
learning. In Chap.6, Jiao et al. present multiple instance learning as a mechanism
toaddressimprecisegroundreferencedatathatiscommonlyencounteredinhyper-
spectralremotesensing.InChap.7,Riseetal.surveysupervised,semi-supervised,
andunsupervisedlearningforhyperspectralregressiontasks.InChap.8,Wuetal.
surveysparse-representation-basedmethodsforhyperspectralimageclassification.
InChap.9,Guetal.reviewmultiplekernellearningforhyperspectralimageclassi-
fication.
Subspace Learning and Feature Selection: Given the high dimensionality of spec-
tralfeaturesandtheinherentinter-channelcorrelationsduetothedense,contiguous
spectralsampling,algorithmsthatlearneffectivesubspaces(e.g.,subspaceswhere
muchofthediscriminativeinformationisretained)andthatlearnthemostrelevant
spectralchannelsareoftenacrucialpre-processingtoimageanalysis.InChap.10,
Zhuetal.presentalow-dimensionalmanifoldmodelforhyperspectralimagerecon-
struction. In Chap.11, Taherkhani et al. present a deep sparse band selection for
hyperspectralfacerecognition.
ChangeandAnomalyDetection:Inmanyapplications,theabilitytoreliablydetect
changes between sets of hyperspectral imagery is highly desirable. In Chap.12,
ZiemannandMatteolipresentrecentdevelopmentstowardrobustdetectionoflarge-
scaleandanomalouschanges.
Spectral Unmixing: The spatialresolutionof hyperspectral imagery acquired from
airborneorspacebornesensorsoftenisnotfineenoughrelativetothesizeofobjectsof
interestinthescene,resultinginmixedpixels.Overrecentyears,numerousadvances
have been made in the area of spectral unmixing—the process of estimating the
relativeabundanceoftheendmembers(e.g.,objectsinthesemixedpixels)ineach
mixedpixel.InChap.13,Zhangetal.reviewrecentadvancesinspectralunmixing
usingsparsetechniquesanddeeplearning.
Image Superresolution: In many remote sensing applications, a common imag-
ing scenario entails simultaneous acquisition of very high spatial resolution color/
multispectral/monochromatic(pan) images and lower spatial resolution hyperspec-
tralimages.Onecanleveragethisbyextractingspatialinformationavailableinthe
higher resolution imagery, which can then be fused in the lower resolution hyper-
spectralimagery.InChap.14,Yangetal.presentadeep-learning-basedapproachto
fusehighspatialresolutionmultispectralimagerywithhyperspectralimagery.
