Chen Compressive Sensing of Earth Observations

1. Auflage 2017
ISBN: 978-1-4987-7438-3
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 384 Seiten

Reihe: Signal and Image Processing of Earth Observations

Future remote sensing systems will make extensive use of Compressive Sensing (CS ) and will include CS as part of the system design. CS can be particularly important with increased high resolution sensor development and the enormous amount of earth observation data. CS exploits the sparsity of the remote sensing data and also allows reduced sampling. It can simplify considerably the remote sensing system design for data acquisition and speed up the process of extracting desired information from a large amount of data. The book provides a comprehensive and balanced coverage of the theory and applications of CS in all aspects of earth observations.

Autoren/Hrsg.

Chen, C.H.

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Geowissenschaften Geologie GIS, Geoinformatik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

Introduction to compressed sensing: theory and practice; Deblurring from highly incomplete measurements for remote sensing; Super-resolution by compressive sensing algorithms; CS-based high resolution imaging tracking of multi-targets and human vital signal detection behind walls; SAR image compression based on sparsity; Compressed sensing applied to weather radar; Application of compressive sensing to atmospheric observations with imaging radar; Compressed sensing for space time signal processing for air-borne radar with high resolution; A novel strategy for radar imaging based on compression sensing; Compressive sensing based subsurface imaging; Subsurface sensing through compressive sensing and virtual experiments; Sonar compressed sensing imaging and localization; Seismic source characterization with compressive sensing; Land use classification with sparse models; Parallel coded aperture method for hyperspectral compressive sensing on GPU; On prototyping and algorithms of compressive imaging instruments.

Über Autor(innen)

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.