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005			20260818145146.0
008			040698 th eng
035			_a.b10395192
099		9	_aAIT Thesis no. CS-92-20
100	1		_aOu, Lien-wei
245	1	0	_aLand use classification based on the combination of binary decision tree and pyramid segmentation techniques
260			_aBangkok : _bAsian Institute of Technology, _c1992
300			_a80 leaves
490	1		_aThesis ; _vno. CS-92-20
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Engineering and Technology
502			_aThesis (M.Eng.) - Asian Institute of Technology, 1992
520			_aThis study combines the pyramid segmentation techniques with binary decision tree algorithm to create accurate and more efficient methods for supervised classification of multispectral images. First, three basic discriminant methods were implemented, namely, maximum likelihood function (MLD), best linear discriminant function (BLD) and then binary decision tree (BDT) is introduced, where the BLD is utilized as a decision rule . Experiments indicate that the decision tree method significantly improves upon the efficiency of classification and also gives satisfactory classification accuracy. Based on these three discriminant functions, pyramid segmentation techniques are applied as the preprocessing procedure. The performance of classification is also obviously improved. Two new pyramid segmentation techniques homogeneous nonoverlap pyramid and efficient overlap pyramid method - are introduced in this study. Both of them much reduce the preprocessing time compared to the conventional overlap pyramid segmentation method and also produce more similar segmented data to the original image. In addition, they can be used as real-time application combined with MLD and BLD, to reduce the storage space and increase the processing speed. All proposed methods have been applied to the MOS-1 (Marine Observ ation Satellite) multispectral images of Bangkok and Chiangmai are a, and good results have been obtained. It is shown that pyramid segmentation coii1bined with binary decision tree classifier is an attractive method for the classification of multispectral images.
650		0	_aImage processing _xDigital techniques _xRemote sensing
700	1		_aHosomura, Tsukasa, _eChairperson
700	1		_aHuynh, Ngoc Phien, _eExamination Committee
700	1		_aSadananda, Ramakoti, _eExamination Committee
710	2		_aThe R.O.C. Government, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. CS-92-20
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B16631
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