Neural Networks in Atmospheric Remote Sensing (Artech House Remote Sensing Library)

232 Pages·2009·4.45 MB·English

by  William J. Blackwell

Neural Networks
in Atmospheric Remote Sensing
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Neural Networks in Atmospheric
Remote Sensing
William J. Blackwell
Frederick W. Chen
Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the U.S. Library of Congress.
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A catalogue record for this book is available from the British Library.
ISBN-13 978-1-59693-372-9
Cover design by Yekaterina Ratner
© 2009 Massachusetts Institute of Technology
Lincoln Laboratory
244 Wood Street
Lexington, MA 02420
All rights reserved.
This work was funded in part by the National Oceanic and Atmospheric Administration
under Air Force contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and
recommendations are those of the authors and are not necessarily endorsed by the United States
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Contents
Preface xiii
1 Introduction 1
1.1 PresentChallenges 1
1.2 SolutionsBasedonNeuralNetworks 2
1.3 MathematicalNotation 3
References 5
2 Physical Background of Atmospheric Remote Sensing 7
2.1 OverviewoftheCompositionandThermalStructure
oftheEarth’sAtmosphere 7
2.1.1 ChemicalCompositionoftheAtmosphere 8
2.1.2 VerticalDistributionofPressureandDensity 9
2.1.3 ThermalStructureoftheAtmosphere 10
2.1.4 CloudMicrophysics 11
2.2 ElectromagneticWavePropagation 12
2.2.1 Maxwell’sEquationsandtheWaveEquation 12
2.2.2 Polarization 13
2.2.3 ReflectionandTransmissionataPlanarBoundary 15
2.3 AbsorptionofElectromagneticWavesbyAtmosphericGases 16
2.3.1 MechanismsofMolecularAbsorption 17
2.3.2 LineShapes 17
2.3.3 AbsorptionCoefficientsandTransmissionFunctions 17
vii
viii NeuralNetworksinAtmosphericRemoteSensing
2.3.4 TheAtmosphericAbsorptionSpectra 18
2.4 ScatteringofElectromagneticWavesbyAtmosphericParticles 19
2.4.1 MieScattering 19
2.4.2 TheRayleighApproximation 21
2.4.3 ComparisonofScatteringandAbsorptionbyHydrometeors 22
2.5 RadiativeTransferinaNonscatteringPlanar-Stratified
Atmosphere 22
2.5.1 EquilibriumRadiation:PlanckandKirchhoff’sLaws 24
2.5.2 RadiativeTransferDuetoEmissionandAbsorption 24
2.5.3 IntegralFormoftheRadiativeTransferEquation 25
2.5.4 WeightingFunction 27
2.6 PassiveSpectrometerSystems 30
2.6.1 OpticalSpectrometers 31
2.6.2 MicrowaveSpectrometers 32
2.7 Summary 33
References 35
3 An Overview of Inversion Problems in Atmospheric
Remote Sensing 37
3.1 MathematicalNotation 38
3.2 Optimality 38
3.3 MethodsThatExploitStatisticalDependence 39
3.3.1 TheBayesianApproach 39
3.3.2 LinearandNonlinearRegressionMethods 41
3.4 PhysicalInversionMethods 45
3.4.1 TheLinearCase 45
3.4.2 TheNonlinearCase 46
3.5 HybridInversionMethods 48
3.5.1 ImprovedRetrievalAccuracy 48
3.5.2 ImprovedRetrievalEfficiency 49
3.6 ErrorAnalysis 49
3.6.1 AnalyticalAnalysis 49
3.6.2 PerturbationAnalysis 50
3.7 Summary 51
References 52
Contents ix
4 Signal Processing and Data Representation 55
4.1 AnalysisoftheInformationContentofHyperspectralData 56
4.1.1 ShannonInformationContent 56
4.1.2 DegreesofFreedom 58
4.2 PrincipalComponentsAnalysis(PCA) 59
4.2.1 NonlinearPCA 61
4.2.2 LinearPCA 61
4.2.3 PrincipalComponentsTransforms 63
4.2.4 TheProjectedPCTransform 64
4.2.5 EvaluationofRadianceCompressionPerformanceUsingTwo
DifferentMetrics 67
4.3 RepresentationofNonlinearFeatures 69
4.4 Summary 70
References 71
5 Introduction to Multilayer Perceptron Neural Networks 73
5.1 ABriefOverviewofMachineLearning 74
5.1.1 SupervisedandUnsupervisedLearning 74
5.1.2 ClassificationandRegression 74
5.1.3 KernelMethods 75
5.1.4 SupportVectorMachines 76
5.1.5 FeedforwardNeuralNetworks 78
5.2 FeedforwardMultilayerPerceptronNeuralNetworks 82
5.2.1 NetworkTopology 82
5.2.2 NetworkTraining 84
5.3 SimpleExamples 85
5.3.1 Single-InputNetworks 85
5.3.2 Two-InputNetworks 93
5.4 Summary 94
5.5 Exercises 95
References 96
6 A Practical Guide to Neural Network Training 97
6.1 DataSetAssemblyandOrganization 97
6.1.1 DataSetIntegrity 98
6.1.2 TheImportanceofanExtensiveandComprehensiveDataSet 98
6.1.3 DataSetPartitioning 98