000			02200cam a2200325 a 4500
003			EG-GiCUC
005			20250223031219.0
008			150426s2014 ua h f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.10.M.Sc.2014.Ah.F
100	0		_aAhmed Mohamed Ahmed Saied Elsheikh
245	1	0	_aFully unsupervised hyperspectral image analysis / _cAhmed Mohamed Ahmed Saied Elsheikh ; Supervised Mohamed H. Farouk
246	1	5	_aتحليل مكتمل بغير إشراف لصور الطيف الفائق
260			_aCairo : _bAhmed Mohamed Ahmed Saied Elsheikh , _c2014
300			_a113 P. : _bfacsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mathematics and Physics
520			_aSpectroscopy is the study of light as a function of wavelength that has been emitted, reflected or scattered from a solid, liquid, or gas. Each material has its own spectral signature, and hence can be identified using spectral analysis. Hyperspectral imaging (HSI) - also called Imaging spectroscopy- sensors observe hundreds or thousands of contiguous spectral bands as well as spatial locality. A hyperspectral image cube (two spatial dimensions and the third is the wavelength) contains a large amount of information about the imaged scenario. Thus the automated analysis of such image cubes is an important asset. Spatial analysis in HSI is rather difficult due to the fact that HSI taken by a satellite or an airborne camera has low ground sampling distance (GSD). This means that many targets of interest can be located within one pixel. Another related problem is the availability of materials in nature as mixtures. As a result, spectral analysis is of great interest specially sub-pixel detection algorithms and spectral unmixing
530			_aIssued also as CD
653		4	_aEndmember extraction
653		4	_aHyperspectral
653		4	_aMaterial count estimation
700	0		_aMohamed Hesham Farouk , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSamia _eCataloger
942			_2ddc _cTH
999			_c50607 _d50607