TY  - BOOK
AU  - Lee,Ha-sook
AU  - Murai,Shunji
AU  - Phan,Minh Dung
AU  - Yulu,Qi
TI  - Classification technique for multi-temporal vegetation index data
T2  - Thesis
PY  - 1993///
CY  - Bangkok
PB  - Asian Institute of Technology
KW  - Remote sensing
KW  - Data processing
KW  - Image processing
KW  - Digital techniques
N1  - A thesis submitted in partial fulfillment of the requirement for the degree of Master of Engineering; Thesis (M.Eng.) - Asian Institute of Technology, 1993
N2  - GVI data were used in this digital image processing study to classify the land cover and to monitor the vegetation dynamics for Asian region. Since this data has multi-bands, in order to reduce the redundancy, Principal Component Analysis was implemented for this multi-temporal data. Color composite images of monthly GVI data were to be useful for visual interpretation of seasonal dynamics. There was a correspondence between seasonal and regional variation of the monthly GVI data. The pattern classification was applied to monthly data for one-year, using the Clustering which is useful for global land cover classification without ground truth. All proposed methods have been applied to GVI images of Asia, and good results have been obtained. Here it is proved that the multi-temporal data can be a suitable method to define the class type and to detect the vegetation changes
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B16142
ER  -