| Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data 2004 Edition Contributor(s): Varshney, Pramod K. (Author), Arora, Manoj K. (Author) |
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ISBN: 3540216685 ISBN-13: 9783540216681 Publisher: Springer OUR PRICE: $265.99 Product Type: Hardcover - Other Formats Published: August 2004 Annotation: The main objective of this book is to apprise the reader of the use of a number of tools and techniques for a variety of image processing tasks, namely Independent Component Analysis (ICA), Mutual Information (MI), Markov Random Field (MRF) Models and Support Vector Machines (SVM). Typical applications considered are feature extraction, image classification, image fusion and change detection. The book also treats a number of experimental examples based on a variety of remote sensors. The utility of the book will be highly appreciated by academicians and R & D professionals, who are involved in current research in the area of hyperspectral imaging, as well as by professional remote-sensing data users such as geologists, hydrologists, environmental scientists, civil engineers and computer scientists. |
| Additional Information |
| BISAC Categories: - Technology & Engineering | Imaging Systems - Technology & Engineering | Remote Sensing & Geographic Information Systems - Technology & Engineering | Electronics - General |
| Dewey: 621.367 |
| LCCN: 2004104167 |
| Physical Information: 0.91" H x 6.44" W x 9.35" (1.54 lbs) 323 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Over the last fifty years, a large number of spaceborne and airborne sensors have been employed to gather information regarding the earth's surface and environment. As sensor technology continues to advance, remote sensing data with improved temporal, spectral, and spatial resolution is becoming more readily available. This widespread availability of enormous amounts of data has necessitated the development of efficient data processing techniques for a wide variety of applications. In particular, great strides have been made in the development of digital image processing techniques for remote sensing data. The goal has been efficient handling of vast amounts of data, fusion of data from diverse sensors, classification for image interpretation, and development of user-friendly products that allow rich visualization. This book presents some new algorithms that have been developed for high- dimensional datasets, such as multispectral and hyperspectral imagery. The contents of the book are based primarily on research carried out by some members and alumni of the Sensor Fusion Laboratory at Syracuse University. |