Advanced Image Processing Techniques For Remotely Sensed Hyperspectral Data
Download Advanced Image Processing Techniques For Remotely Sensed Hyperspectral Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Advanced Image Processing Techniques For Remotely Sensed Hyperspectral Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data
Author: Pramod K. Varshney
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-09
The first of its kind, this book reviews image processing tools and techniques including Independent Component Analysis, Mutual Information, Markov Random Field Models and Support Vector Machines. The book also explores a number of experimental examples based on a variety of remote sensors. The book will be useful to people involved in hyperspectral imaging research, as well as by remote-sensing data like geologists, hydrologists, environmental scientists, civil engineers and computer scientists.
Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data
Author: Pramod K. Varshney
language: en
Publisher: Springer Science & Business Media
Release Date: 2004-08-12
The first of its kind, this book reviews image processing tools and techniques including Independent Component Analysis, Mutual Information, Markov Random Field Models and Support Vector Machines. The book also explores a number of experimental examples based on a variety of remote sensors. The book will be useful to people involved in hyperspectral imaging research, as well as by remote-sensing data like geologists, hydrologists, environmental scientists, civil engineers and computer scientists.
Remote Sensing
This book is a completely updated, greatly expanded version of the previously successful volume by the author. The Second Edition includes new results and data, and discusses a unified framework and rationale for designing and evaluating image processing algorithms.Written from the viewpoint that image processing supports remote sensing science, this book describes physical models for remote sensing phenomenology and sensors and how they contribute to models for remote-sensing data. The text then presents image processing techniques and interprets them in terms of these models. Spectral, spatial, and geometric models are used to introduce advanced image processing techniques such as hyperspectral image analysis, fusion of multisensor images, and digital elevationmodel extraction from stereo imagery.The material is suited for graduate level engineering, physical and natural science courses, or practicing remote sensing scientists. Each chapter is enhanced by student exercises designed to stimulate an understanding of the material. Over 300 figuresare produced specifically for this book, and numerous tables provide a rich bibliography of the research literature.