The development of a neural-network-based classifier for classifying three distinct scenes (urban, park, and water) from several polarized SAR images of the San Francisco Bay area is discussed. The principal components (PC) scheme or Karhunen-Loeve 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. Using the PC scheme along with the polarized images used in the present study led to substantial improvements in the classification rates when compared with previous studies. When a combined polarization architecture was used, the classification rate for water, urban, and park areas improved to 100%, 98.7%, and 96.1%, respectively.

Meeting Name

40th IAS Annual Meeting of the Industry Applications Conference, 2005


Electrical and Computer Engineering

Keywords and Phrases

Karhunen-Loeve Transform; SAR Images; San Francisco Bay Area; Classification Rates; Combined Polarization Architecture; Dimensionality; Feature Space; Geophysics Computing; Image Processing; Neural Nets; Neural Networks; Park; Polarized Images; Principal Components Analysis; Remote Sensing; Remote Sensing By Radar; Surface Water Areas; Terrain; Urban Areas; Microwave and Millimeter Wave Imaging

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version

Final Version

File Type





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