Automatic Hyperspectral Data Analysis | A machine learning approach to high dimensional feature extraction

Sildomar Monteiro

ISBN 10: 363925516X / ISBN 13: 9783639255164

Published by VDM Verlag Dr. Müller, 2010

About this Item

Automatic Hyperspectral Data Analysis | A machine learning approach to high dimensional feature extraction | Sildomar Monteiro | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2010 | VDM Verlag Dr. Müller | EAN 9783639255164 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Seller Inventory # 101097327

Bibliographic Details

• Title: Automatic Hyperspectral Data Analysis | A machine learning approach to high dimensional feature extraction
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 363925516X
• ISBN 13: 9783639255164
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 160 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Advances in spectroscopy sensors have allowed the acquisition of ever-increasing volumes of data from scenes, either remotely, by air- or space-borne devices, or locally, by hand-held spectrometers or stand-alone cameras. With this boom in the amount of data available has also come a greater need for extracting useful information efficiently and for developing automated methods for novel applications. Traditional approaches to spectral analysis often require a great deal of human effort and prior knowledge, and have difficulty in processing high dimensional data sets provided by new sensors. This book, therefore, provides an alternative approach to select relevant features from hyperspectral data utilizing machine learning to automate the analysis. The methods are developed in the context of two applications: in biomedical imaging and in precision agriculture. The techniques discussed should be useful to graduate students and researchers in computer science and engineering interested in hyperspectral imaging, remote sensing or optimization for high dimensional data.

About the Author

Sildomar Monteiro is Research Fellow in the Australian Centre for Field Robotics at Sydney University. He was awarded a JSPS postdoctoral fellowship in 2007. His research areas include machine learning, computer vision, signal processing, and applications to robotics and remote sensing.