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Terrain classification in SAR images using principal components analysis and neural networks




Zoughi, R., author
Ghaloum, S., author
Azimi-Sadjadi, Mahmood R., author
IEEE, publisher

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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.


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image processing
geophysics computing
neural nets
remote sensing by radar


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