Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Advanced image processing techniques for remotely sensed hyperspectral data : with 30 tables
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. TOC: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
Advanced image processing techniques for remotely sensed hyperspectral data : with 30 tables
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. TOC: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
Advanced image processing techniques for remotely sensed hyperspectral data : with 30 tables
Varshney, Pramod K. (Autor:in) / Arora, Manoj K. (Autor:in)
2004
XV, 322 S
Ill., graph. Darst
Literaturangaben
Langzeitarchivierung durch Badische Landesbibliothek
Report
Englisch
Atmosphere - Measuring Trace Gases in Plumes From Hyperspectral Remotely Sensed Data
| Online Contents | 2004
Feature-Driven Multilayer Visualization for Remotely Sensed Hyperspectral Imagery
| Online Contents | 2010
Active-Metric Learning for Classification of Remotely Sensed Hyperspectral Images
| Online Contents | 2016
FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis
| Online Contents | 2012
Computer processing of remotely-sensed images
| TIBKAT | 2022