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.
Preface ; Introduction  Present Challenges. Solutions Based on Neural Networks. Mathematical Notation. ; Physical Background of Atmospheric Remote Sensing  Overview of the Composition and Thermal Structure of the Earth 's Atmosphere. Electromagnetic Wave Propagation. Absorption of Electromagnetic Waves by Atmospheric Gases. Scattering of Electromagnetic Waves by Atmospheric Particles. Radiative Transfer in a Nonscattering PlanarStratified Atmosphere. Passive Spectrometer Systems. Summary. ; An Overview of Inversion Problems in Atmospheric Remote Sensing  Mathematical Notation. Optimality. Methods That Exploit Statistical Dependence. Physical Inversion Methods. Hybrid Inversion Methods. Error Analysis. Summary. ; Signal Processing and Data Representation  Analysis of the Information Content of Hyperspectral Data. Principal Components Analysis (PCA). Representation of Nonlinear Features. Summary. ; Introduction to Multilayer Perceptron Neural Networks  A Brief Overview of Machine Learning. Feedforward Multilayer Perceptron Neural Networks. Simple Examples. Summary. Exercises. ; A Practical Guide to Neural Network Training  Data Set Assembly and Organization. Model Selection. Network Initialization. Network Training. Underfitting and Overfitting. Regularization Techniques. Performance Evaluation. Summary. ; Pre and PostProcessing of Atmospheric Data  Mathematical Overview. Data Compression. Filtering of Interfering Signals. Data Warping. Summary.; Neural Network Jacobian Analysis  Calculation of the Neural Network Jacobian. Neural Network Error Analysis Using the Jacobian. Retrieval System Optimization Using the Jacobian. Summary. ; Neural Network Retrieval of Precipitation from Passive Microwave Observations  Structure of the Algorithm. Signal Processing Components. Development of the Algorithm. Retrieval Performance Evaluation. Summary.; Neural Network Retrieval of Atmospheric Profiles from Microwave and Hyperspectral Infrared Observations  The PPC/NN Algorithm. Retrieval Performance Comparisons with Simulated ClearAir AIRS Radiances. Validation of the PPC/NN Algorithm with AIRS/AMSU Observations of Partially Cloudy Scenes over Land and Ocean. Summary and Conclusions. ; Discussion of Future Work  Bayesian Approaches for Neural Network Training and Error Characterization. Soft Computing: NeuroFuzzy Systems. Nonstationarity Considerations: Neural Network Applications for Climate Studies. ; About the Authors. Index ;

William J. Blackwell
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
Frederick W. Chen was most recently a technical staff member at the MIT Lincoln Laboratory, where he worked on problems in satellitebased 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.