Neural Networks In Atmospheric Remote Sensing
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Neural Networks in Atmospheric Remote Sensing
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.
Signal and Image Processing for Remote Sensing
Advances in signal and image processing for remote sensing have been tremendous in recent years. The progress has been particularly significant with the use of deep learning based techniques to solve remote sensing problems. These advancements are the focus of this third edition of Signal and Image Processing for Remote Sensing. It emphasizes the use of machine learning approaches for the extraction of remote sensing information. Other topics include change detection in remote sensing and compressed sensing. With 19 new chapters written by world leaders in the field, this book provides an authoritative examination and offers a unique point of view on signal and image processing. Features Includes all new content and does not replace the previous edition Covers machine learning approaches in both signal and image processing for remote sensing Studies deep learning methods for remote sensing information extraction that is found in other books Explains SAR, microwave, seismic, GPR, and hyperspectral sensors and all sensors considered Discusses improved pattern classification approaches and compressed sensing approaches Provides ample examples of each aspect of both signal and image processing This book is intended for university academics, researchers, postgraduate students, industry, and government professionals who use remote sensing and its applications.
Atmospheric Remote Sensing
Atmospheric Remote Sensing: Principles and Applications discusses the fundamental principles of atmospheric remote sensing and their applications in different research domains. Furthermore, the book covers the basic concepts of satellite remote sensing of the atmosphere, followed by Ionospheric remote sensing tools like Global Positioning System (GPS) and Very Low Frequency (VLF) wave. Sections emphasize the applications of atmospheric remote study in Ionospheric perturbation, fire detection, aerosol characteristics over land, ocean and Himalayan regions. In addition, the application of atmospheric remote sensing in disaster management like dust storms, cyclones, smoke plume, aerosol-cloud interaction, and their impact on climate change are discussed. This book is a valuable reference for students, researchers and professionals working in atmospheric science, remote sensing, and related disciplines. - Covers the fundamentals of remote sensing as applied to atmospheric science - Includes methods and applications of remote sensing technologies for atmospheric science and related disciplines in earth science - Includes full color photographs and figures that visually represent concepts discussed in the book