Compressive sensing of earth observations
Author(s)
Bibliographic Information
Compressive sensing of earth observations
（Signal and image processing of Earth observations series）
CRC Press, c2017
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Future remote sensing systems will make extensive use of Compressive Sensing (CS) as it becomes more integrated into the system design with increased high resolution sensor developments and the rising earth observation data generated each year. Written by leading experts in the field Compressive Sensing of Earth Observations provides a comprehensive and balanced coverage of the theory and applications of CS in all aspects of earth observations. This work covers a myriad of practical aspects such as the use of CS in detection of human vital signs in a cluttered environment and the corresponding modeling of rib-cage breathing. Readers are also presented with three different applications of CS to the ISAR imaging problem, which includes image reconstruction from compressed data, resolution enhancement, and image reconstruction from incomplete data.
Table of Contents
Preface. Editor. Contributors. 1 Compressed Sensing: From Theory to Praxis. 2 Compressive Sensing on the Sphere: Slepian Functions for Applications in Geophysics. 3 Compressive Sensing-Based High Resolution Imaging and Tracking of Targets and Human Vital Sign Detection behind Walls. 4 Recovery Guarantees for High Resolution Radar Sensing with Compressive Illumination. 5 Compressive Sensing for Inverse Synthetic Aperture Radar Imaging. 6 A Novel Compressed Sensing-Based Algorithm for Space-Time Signal Processing Using Airborne Radars. 7 Bayesian Sparse Estimation of Radar Targets in the Compressed Sensing Framework. 8 Virtual Experiments and Compressive Sensing for Subsurface Microwave Tomography. 9 Seismic Source Monitoring with Compressed Sensing. 10 Seismic Data Regularization and Imaging Based on Compressive Sensing and Sparse Optimization. 11 Land Use Classification with Sparse Models. 12 Compressive Sensing for Reconstruction, Classification, and Detection of Hyperspectral Images. 13 Structured Abundance Matrix Estimation for Land Cover Hyperspectral Image Unmixing. 14 Parallel Coded Aperture Method for Hyperspectral Compressive Sensing on GPU. 15 Algorithms and Prototyping of a Compressive Hyperspectral Imager. Index.
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