Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data

By: Pramod K. Varshney, M. K. Arora

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Hardcover | 12 August 2004

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340 Pages

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23.5 x 15.88 x 2.54

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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.

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You Can Find This Book In

Non-Fiction Earth Sciences, Geography, Environment, Planning Earth Sciences Computing & I.T.Computer Science Image Processing Geography Geographical Information Systems GIS & Remote Sensing Engineering & Technology Electronics & Communications Engineering Electronics Engineering Energy Technology & Engineering

Electrical Engineering Other Technologies & Applied Sciences Applied Optics Laser Technology & Holography

Hyperspectral Sensors and Applications
Overview of Image Processing
Mutual Information: A Similarity Measure for Intensity Based Image Registration
Independent Component Analysis
Support Vector Machines
Markov Random Field Models
Applications: MI Based Registration of Multi-Sensor and Multi-Temporal Images
Feature Extraction from Hyperspectral Data Using ICA
Hyperspectral Classification using ICA Based Mixture Model
Support Vector Machines for Classification of Multi- and Hyperspectral Data
An MRF Model Based Approach for Sub-pixel Mapping from Hyperspectral Data
Image Change Detection and Fusion Using MRF Models
Table of Contents provided by Publisher. All Rights Reserved.

Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data

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