Dr. Pei-Gee Ho dissertation | MULTIVARIATE TIME SERIES MODEL BASED SUPPORT VECTOR MACHINE FOR MULTICLASS REMOTE SENSING IMAGE CLASSIFICATION AND REGION SEGMENTATION

Pei-Gee Ho

ISBN 10: 3838303520 / ISBN 13: 9783838303529

Published by LAP LAMBERT Academic Publishing, 2009

About this Item

Dr. Pei-Gee Ho dissertation | MULTIVARIATE TIME SERIES MODEL BASED SUPPORT VECTOR MACHINE FOR MULTICLASS REMOTE SENSING IMAGE CLASSIFICATION AND REGION SEGMENTATION | Pei-Gee Ho | Taschenbuch | 124 S. | Englisch | 2009 | LAP LAMBERT Academic Publishing | EAN 9783838303529 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Dr. Pei-Gee Ho dissertation | MULTIVARIATE TIME SERIES MODEL BASED SUPPORT VECTOR MACHINE FOR MULTICLASS REMOTE SENSING IMAGE CLASSIFICATION AND REGION SEGMENTATION
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2009
• Language: English
• ISBN 10: 3838303520
• ISBN 13: 9783838303529
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 203 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Satellite and airborne Remote Sensing for observing the earth surface, land monitoring and geographical information systems control are issues in world’s daily life. The source of information was primarily acquired by imaging sensors and spectroradiometer in remote sensing multi-spectral image stack format. The contextual information between pixels or pixel vectors is characterized by a time series model for image processing in the remote sensing. Due to the nature of remote sensing images such as SAR and TM which are mostly in multi-spectral image stack format, a 2-D Multivariate Vector AR (ARV) time series model with pixel vectors of multiple elements are formulated. To compute the time series ARV system parameter matrix and estimate the error covariance matrix efficiently, a new method based on modern numerical analysis is developed. As for pixel classification, the powerful Support Vector Machine (SVM) kernel based learning machine is applied. The 2-D multivariate time series model is particularly suitable to capture the rich contextual information in single and multiple images at the same time.

About the Author

Pei-Gee Peter Ho received his BSEE from NCKU, Taiwan in 1976 and MSEE from UMass Dartmouth in 1981. During the last 20 plus years he has worked in various computer engineering companies. He received his Ph.D. degree in EE from UMass Dartmouth in January 2008. He is now working in the DSP algorithm group of NUWC at Newport, Rhode Island.