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040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aPh.D
099 _aCai01.13.05.Ph.D.2019.Ah.N
100 0 _aAhmed Abdo Nasr Habib
245 1 0 _aNew hybrid technique for geometric correction of high resolution satellite imagery /
_cAhmed Abdo Nasr Habib ; Supervised Zeinab Abdelghany Wishahy , Mohamed Shawki Elghazaly
246 1 5 _aتقنية جديدة دمجية للتصحيح الهندسي لصور الأقمار الصناعية عالية الدقة
260 _aCairo :
_bAhmed Abdo Nasr Habib ,
_c2019
300 _a78 P. :
_bill. ;
_c30cm
502 _aThesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Civil Engineering
520 _aAn Artificial neural networks (ANN) MATLAB software was developed with multi-layer perceptron (MLP) technique to derive the geometric correction coefficients. The Artificial neural network training was done using the deduced control points in a way that, image coordinates were used as input and the ground coordinates as output till reaching stabilization state of the neural network parameters. A change in the nature of the distribution of errors has been noted, as a result of the numerical stability of the neural network. A new technique was developed using neural networks to predict the earth coordinates of a set of new regular image points in the same area of the deduced random point{u2019}s data set and a new DDSM model. The RFM model was reused by implementing regularized points to reach the final model coefficients between satellite imagery space domain and ground space domain. The new technology improved accuracy by reducing the planimetric error by 39% and the elevation error by 45% of the error recorded when using traditional RFM model
530 _aIssued also as CD
653 4 _aHigh Resolution Satellite
653 4 _aRational Function Model
653 4 _aRemote Sensing
700 0 _aMohamed Shawki Elghazaly ,
_eSupervisor
700 0 _aZeinab Abdelghany Wishahy ,
_eSupervisor
856 _uhttp://172.23.153.220/th.pdf
905 _aNazla
_eRevisor
905 _aShimaa
_eCataloger
942 _2ddc
_cTH
999 _c74322
_d74322