Neural Networks in Atmospheric Remote Sensing [With CDROM]

Hardcover (Print)
Used and New from Other Sellers
Used and New from Other Sellers
from $101.17
Usually ships in 1-2 business days
(Save 7%)
Other sellers (Hardcover)
  • All (1) from $101.17   
  • Used (1) from $101.17   

Overview

This authoritative reference offers you a comprehensive understanding of the underpinnings and practical applications of artificial neural networks and their use in the retrieval of geophysical parameters. You find expert guidance on the development and evaluation of neural network algorithms that process data from a new generation of hyperspectral sensors. The book provides clear explanations of the mathematical and physical foundations of remote sensing systems, including radiative transfer and propagation theory, sensor technologies, and inversion and estimation approaches. You discover how to use neural networks to approximate remote sensing inverse functions with emphasis on model selection, preprocessing, initialization, training, and performance evaluation.

Read More Show Less

Product Details

  • ISBN-13: 9781596933729
  • Publisher: Artech House, Incorporated
  • Publication date: 7/31/2009
  • Edition description: New Edition
  • Pages: 215
  • Product dimensions: 6.20 (w) x 9.30 (h) x 0.70 (d)

Meet the Author

William J. Blackwell is on the technical staff at the MIT Lincoln Laboratory and is currently a science team member involved with atmospheric sounding systems aboard NPOESS and NASA EOS/NPP Missions. He received an S.M. and Sc.D. in electrical engineering from the Massachusetts Institute of Technology. Frederick W. Chen was most recently a technical staff member at the MIT Lincoln Laboratory, where he worked on problems in satellite-based atmospheric remote sensing using microwave and infrared data. He holds an S.B., M.Eng., and Ph.D. in electrical engineering from the Massachusetts Institute of Technology. David H. Staelin is a professor of electrical engineering in the Research Laboratory of Electronics at MIT. He holds an S.B., S.M., and Sc.D. in electrical engineering from the Massachusetts Institute of Technology.

Read More Show Less

Table of Contents

Preface xiii

1 Introduction 1

1.1 Present Challenges 1

1.2 Solutions Based on Neural Networks 2

1.3 Mathematical Notation 3

References 5

2 Physical Background of Atmospheric Remote Sensing 7

2.1 Overview of the Composition and Thermal Structure of the Earth's Atmosphere 7

2.1.1 Chemical Composition of the Atmosphere 8

2.1.2 Vertical Distribution of Pressure and Density 9

2.1.3 Thermal Structure of the Atmosphere 10

2.1.4 Cloud Microphysics 11

2.2 Electromagnetic Wave Propagation 12

2.2.1 Maxwell's Equations and the Wave Equation 12

2.2.2 Polarization 13

2.2.3 Reflection and Transmission at a Planar Boundary 15

2.3 Absorption of Electromagnetic Waves by Atmospheric Gases 16

2.3.1 Mechanisms of Molecular Absorption 17

2.3.2 Line Shapes 17

2.3.3 Absorption Coefficients and Transmission Functions 17

2.3.4 The Atmospheric Absorption Spectra 18

2.4 Scattering of Electromagnetic Waves by Atmospheric Particles 19

2.4.1 Mie Scattering 19

2.4.2 The Rayleigh Approximation 21

2.4.3 Comparison of Scattering and Absorption by Hydrometeors 22

2.5 Radiative Transfer in a Nonscattering Planar-Stratified Atmosphere 22

2.5.1 Equilibrium Radiation: Planck and Kirchhoff's Laws 24

2.5.2 Radiative Transfer Due to Emission and Absorption 24

2.5.3 Integral Form of the Radiative Transfer Equation 25

2.5.4 Weighting Function 27

2.6 Passive Spectrometer Systems 30

2.6.1 Optical Spectrometers 31

2.6.2 Microwave Spectrometers 32

2.7 Summary 33

References 35

3 An Overview of Inversion Problems in Atmospheric Remote Sensing 37

3.1 Mathematical Notation 38

3.2 Optimality 38

3.3 Methods That Exploit Statistical Dependence 39

3.3.1 TheBayesian Approach 39

3.3.2 Linear and Nonlinear Regression Methods 41

3.4 Physical Inversion Methods 45

3.4.1 The Linear Case 45

3.4.2 The Nonlinear Case 46

3.5 Hybrid Inversion Methods 48

3.5.1 Improved Retrieval Accuracy 48

3.5.2 Improved Retrieval Efficiency 49

3.6 Error Analysis 49

3.6.1 Analytical Analysis 49

3.6.2 Perturbation Analysis 50

3.7 Summary 51

References 52

4 Signal Processing and Data Representation 55

4.1 Analysis of the Information Content of Hyperspectral Data 56

4.1.1 Shannon Information Content 56

4.1.2 Degrees of Freedom 58

4.2 Principal Components Analysis (PCA) 59

4.2.1 Nonlinear PCA 61

4.2.2 Linear PCA 61

4.2.3 Principal Components Transforms 63

4.2.4 The Projected PC Transform 64

4.2.5 Evaluation of Radiance Compression Performance Using Two Different Metrics 67

4.3 Representation of Nonlinear Features 69

4.4 Summary 70

References 71

5 Introduction to Multilayer Perceptron Neural Networks 73

5.1 A Brief Overview of Machine Learning 74

5.1.1 Supervised and Unsupervised Learning 74

5.1.2 Classification and Regression 74

5.1.3 Kernel Methods 75

5.1.4 Support Vector Machines 76

5.1.5 Feedforward Neural Networks 78

5.2 Feedforward Multilayer Perceptron Neural Networks 82

5.2.1 Network Topology 82

5.2.2 Network Training 84

5.3 Simple Examples 85

5.3.1 Single-Input Networks 85

5.3.2 Two-Input Networks 93

5.4 Summary 94

5.5 Exercises 95

References 96

6 A Practical Guide to Neural Network Training 97

6.1 Data Set Assembly and Organization 97

6.1.1 Data Set Integrity 98

6.1.2 The Importance of an Extensive and Comprehensive Data Set 98

6.1.3 Data Set Partitioning 98

6.2 Model Selection 100

6.2.1 Number of Inputs 100

6.2.2 Number of Hidden Layers and Nodes 100

6.2.3 Adaptive Model Building Techniques 101

6.3 Network Initialization 101

6.4 Network Training 102

6.4.1 Calculation of the Error Gradient Using Backpropagation 102

6.4.2 First-Order Optimization: Gradient Descent 104

6.4.3 Second-Order Optimization: Levenberg-Marquardt 104

6.5 Underfitting and Overfitting 105

6.6 Regularization Techniques 107

6.6.1 Treatment of Noisy Data 108

6.6.2 Weight Decay 110

6.7 Performance Evaluation 111

6.8 Summary 112

References 114

7 Pre-and Post-Processing of Atmospheric Data 115

7.1 Mathematical Overview 116

7.2 Data Compression 117

7.3 Filtering of Interfering Signals 118

7.3.1 The Wiener Filter 119

7.3.2 Stochastic Cloud Clearing 120

7.4 Data Warping 124

7.4.1 Function of Time of Day 125

7.4.2 Function of Geolocation 129

7.4.3 Function of Time of Year 131

7.5 Summary 134

References 135

8 Neural Network Jacobian Analysis 137

8.1 Calculation of the Neural Network Jacobian 138

8.2 Neural Network Error Analysis Using the Jacobian 139

8.2.1 The Network Weight Jacobian 139

8.2.2 The Network Input Jacobian 140

8.2.3 Use of the Jacobian to Assess Noise Contribution 141

8.3 Retrieval System Optimization Using the Jacobian 143

8.3.1 Noise Smoothing Versus Atmospheric Smoothing 144

8.3.2 Optimization Approach 145

8.3.3 Optimization Results 146

8.4 Summary 146

References 148

9 Neural Network Retrieval of Precipitation from Passive Microwave Observations 149

9.1 Structure of the Algorithm 149

9.1.1 Physical Basis of Preprocessing 150

9.1.2 Physical Basis of Post-Processing 153

9.2 Signal Processing Components 153

9.2.1 Limb-and-Surface Corrections 153

9.2.2 Precipitation Detection 155

9.2.3 Cloud Clearing by Regional Laplacian Interpolation 159

9.2.4 Temperature-Profile and Water-Vapor-Profile Principal Components 163

9.2.5 Image Sharpening 164

9.3 Development of the Algorithm 165

9.4 Retrieval Performance Evaluation 168

9.4.1 Image Comparisons of Nexrad and Amsu/Hsb 168

9.4.2 Numerical Comparisons of Nexrad and Amsu/Hsb Retrievals 169

9.4.3 Global Retrievals of Rain and Snow 173

9.5 Summary 175

References 176

10 Neural Network Retrieval of Atmospheric Profiles from Microwave and Hyperspectral Infrared Observations 179

10.1 The Ppc/Nn Algorithm 180

10.1.1 Network Topology 181

10.1.2 Network Training 181

10.2 Retrieval Performance Comparisons with Simulated Clear-Air Airs Radiances 181

10.2.1 Simulation of Airs Radiances 182

10.2.2 An Iterated Minimum-Variance Technique for the Retrieval of Atmospheric Profiles 183

10.2.3 Retrieval Performance Comparisons 184

10.2.4 Discussion 185

10.3 Validation of the Ppc/Nn Algorithm with Airs/Amsu Observations of Partially Cloudy Scenes over Land and Ocean 188

10.3.1 Cloud Clearing of Airs Radiances 188

10.3.2 Airs/Amsu/Ecmwf Data Set 188

10.3.3 Airs/Amsu Channel Selection 189

10.3.4 Ppc/Nn Retrieval Enhancements for Variable Sensor Scan Angle and Surface Pressure 189

10.3.5 Retrieval Performance 190

10.3.6 Retrieval Performance Sensitivity Analyses 194

10.3.7 Discussion and Future Work 198

10.4 Summary and Conclusions 201

References 202

11 Discussion of Future Work 205

11.1 Bayesian Approaches for Neural Network Training and Error Characterization 205

11.2 Soft Computing: Neuro-Fuzzy Systems 206

11.3 Nonstationarity Considerations: Neural Network Applications for Climate Studies 207

References 209

About the Authors 211

Index 213

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)