| Description |
1 online resource (xv, 215 pages) : illustrations |
| Contents |
""Neural Networks in Atmospheric Remote Sensing""; ""Contents""; ""Preface""; ""1 Introduction""; ""1.1 Present Challenges""; ""1.2 Solutions Based on Neural Networks""; ""1.3 Mathematical Notation""; ""References""; ""2 Physical Background of Atmospheric Remote Sensing""; ""2.1 Overview of the Composition and Thermal Structure of the Earth�s Atmosphere""; ""2.1.1 Chemical Composition of the Atmosphere""; ""2.1.2 Vertical Distribution of Pressure and Density""; ""2.1.3 Thermal Structure of the Atmosphere""; ""2.1.4 Cloud Microphysics""; ""2.2 Electromagnetic Wave Propagation"" |
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""2.2.1 Maxwell�s Equations and the Wave Equation""""2.2.2 Polarization""; ""2.2.3 Reflection and Transmission at a Planar Boundary""; ""2.3 Absorption of Electromagnetic Waves by Atmospheric Gases""; ""2.3.1 Mechanisms of Molecular Absorption""; ""2.3.2 Line Shapes""; ""2.3.3 Absorption Coefficients and Transmission Functions""; ""2.3.4 The Atmospheric Absorption Spectra""; ""2.4 Scattering of Electromagnetic Waves by Atmospheric Particles""; ""2.4.1 Mie Scattering""; ""2.4.2 The Rayleigh Approximation""; ""2.4.3 Comparison of Scattering and Absorption byHydrometeors"" |
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""2.5 Radiative Transfer in a Nonscattering Planar-Stratified Atmosphere""""2.5.1 Equilibrium Radiation: Planck and Kirchhoff�s Laws""; ""2.5.2 Radiative Transfer Due to Emission and Absorption""; ""2.5.3 Integral Form of the Radiative Transfer Equation""; ""2.5.4 Weighting Function""; ""2.6 Passive Spectrometer Systems""; ""2.6.1 Optical Spectrometers""; ""2.6.2 Microwave Spectrometers""; ""2.7 Summary""; ""References""; ""3 An Overview of Inversion Problems in Atmospheric Remote Sensing""; ""3.1 Mathematical Notation""; ""3.2 Optimality"" |
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3.3 Methods That Exploit Statistical Dependence3.3.1 The Bayesian Approach -- 3.3.2 Linear and Nonlinear Regression Methods -- 3.4 Physical Inversion Methods -- 3.4.1 The Linear Case -- 3.4.2 The Nonlinear Case -- 3.5 Hybrid Inversion Methods -- 3.5.1 Improved Retrieval Accuracy -- 3.5.2 Improved Retrieval Efficiency -- 3.6 Error Analysis -- 3.6.1 Analytical Analysis -- 3.6.2 Perturbation Analysis -- 3.7 Summary -- References -- 4 Signal Processing and Data Representation -- 4.1 Analysis of the Information Content of Hyperspectral Data |
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4.1.1 Shannon Information Content4.1.2 Degrees of Freedom -- 4.2 Principal Components Analysis (PCA) -- 4.2.1 Nonlinear PCA -- 4.2.2 Linear PCA -- 4.2.3 Principal Components Transforms -- 4.2.4 The Projected PC Transform -- 4.2.5 Evaluation of Radiance Compression Performance Using Two Different Metrics -- 4.3 Representation of Nonlinear Features -- 4.4 Summary -- References -- 5 Introduction to Multilayer Perceptron Neural Networks -- 5.1 A Brief Overview of Machine Learning -- 5.1.1 Supervised and Unsupervised Learning -- 5.1.2 Classification and Regression |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Neural networks (Computer science)
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Atmosphere -- Remote sensing
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Microwave remote sensing.
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Thermography.
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infrared thermography.
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COMPUTERS -- Neural Networks.
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Atmosphere -- Remote sensing
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Microwave remote sensing
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Neural networks (Computer science)
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Thermography
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| Form |
Electronic book
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| Author |
Chen, Frederick W.
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| ISBN |
9781596933736 |
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1596933739 |
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