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035 _a.b10141212
099 9 _aAIT Thesis no.CS-93-32
100 1 _aLee, Ha-sook
245 1 0 _aClassification technique for multi-temporal vegetation index data
260 _aBangkok :
_bAsian Institute of Technology,
_c1993
300 _a55 leaves +
_e1 online resource
490 1 _aThesis ;
_vno. CS-93-32
500 _aA thesis submitted in partial fulfillment of the requirement for the degree of Master of Engineering
502 _aThesis (M.Eng.) - Asian Institute of Technology, 1993
520 _aGVI 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.
650 0 _aRemote sensing
_xData processing
650 0 _aImage processing
_xDigital techniques
700 1 _aMurai, Shunji,
_eChairperson
700 1 _aPhan, Minh Dung,
_eExamination Committee
700 1 _aYulu, Qi,
_eExamination committee
810 2 _aAsian Institute of Technology.
_tThesis ;
_vno. CS-93-32
856 _3Full-Text
_uhttp://203.159.5.9/ait-thesis/detail.php?q=B16142
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