Terrain classification in SAR images using principal components analysis and neural networks Azimi-Sadjadi, Mahmood R. ; Ghaloum, S. ; Zoughi, R. The development of a neural network-based classifier for classifying three distinct scenes (urban, park and water) from several polarized SAR images of San Francisco Bay area is discussed. The principal component (PC) scheme or Karhunen-Loeve (KL) transform is used to extract the salient features of the input data, and to reduce the dimensionality of the feature space prior to the application to the neural networks. Employing PC scheme along with polarized images used in this study, led to substantial improvements in the classification rates when compared with previous studies. When a combined polarization architecture is used the classification rate for water, urban and park areas improved to 100%, 98.7%, and 96.1%, respectively. Colorado State University. Libraries 1993 text ; image application/pdf ECEmra00058.pdf FACFECEN100538ARTI eng c1993 IEEE
Terrain classification in SAR images using principal components analysis and neural networks
Azimi-Sadjadi, Mahmood R. ; Ghaloum, S. ; Zoughi, R.
The development of a neural network-based classifier for classifying three distinct scenes (urban, park and water) from several polarized SAR images of San Francisco Bay area is discussed. The principal component (PC) scheme or Karhunen-Loeve (KL) transform is used to extract the salient features of the input data, and to reduce the dimensionality of the feature space prior to the application to the neural networks. Employing PC scheme along with polarized images used in this study, led to substantial improvements in the classification rates when compared with previous studies. When a combined polarization architecture is used the classification rate for water, urban and park areas improved to 100%, 98.7%, and 96.1%, respectively.
Colorado State University. Libraries
1993
text ; image
application/pdf
ECEmra00058.pdf
FACFECEN100538ARTI
eng
c1993 IEEE