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This book provides information on the Earth science remote sensing data information and data format such as HDF-EOS. It evaluates the current data processing approaches and introduces data searching and ordering from different public domains. It further explores the remote sensing and GIS migration products and WebGIS applications. Both volumes are designed to give an introduction to current and future NASA, NOAA and other Earth science remote sensing.
The system of satellites in place to provide environmental data-data to monitor events such as forest fires and floods; to make weather predictions; and to assess crops, transportation impacts, fisheries, land-use patterns, sea temperature, and soil moisture, among other things- serves a wide and growing array of users. In the coming years as the next generation of operational environmental satellites in put in orbit, the will be a large expansion in data availability. To ensure that these data serve effectively this broad user community, a new vision for the future of operational environmental satellite data utilization is needed. To help develop approaches for handling this potential data overload, NASA, with technical support from NOAA, asked the NRC to conduct an end-to-end review of issues about the utilization of operational environmental satellite data for 2010 and beyond. This report presents the result of that review. It focuses on ensuring the value of environmental satellite data for addressing specific user needs, distribution of such data, and data access and utilization.
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.
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.