Compressive Sensing of Earth Observations

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.

Chi Hau Chen (IEEE Life Fellow 2003, IEEE Fellow 1988) received his Ph.D. in electrical engineering from Purdue University in 1965. He has been a faculty member with the University of Massachusetts Dartmouth (UMass Dartmouth) since1968 where he is currently Chancellor Professor Emeritus. Dr. Chen was the Associate Editor of IEEE Trans. on Acoustics, Speech and Signal Processing from 1982 to 1986, Associate Editor on information processing of IEEE Trans. on Geoscience and Remote Sensing 1985 to 2000. He is also a Fellow of International Association of Pattern Recognition (IAPR, 1966) and a editorial Board Member of Pattern Recognition Journal since 2008. He is a book series editor for CRC Press on Signal and Image Processing with Earth Observations. In addition to the theory and applications of statistical pattern recognition, his research has included the signal and image processing of underwater acoustic and geophysical signals, and ultrasonic data for nondestructive evaluation, as well as remote sensing and medical imaging. He has published 34 (authored and edited) books in his areas of research interest.

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.