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.
M. R. Azimi-Sadjadi et al., "Terrain Classification in SAR Images using Principal Components Analysis and Neural Networks," IEEE Transactions on Geoscience and Remote Sensing, vol. 31, no. 2, pp. 511-515, Institute of Electrical and Electronics Engineers (IEEE), Mar 1993.
The definitive version is available at http://dx.doi.org/10.1109/36.214928
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)
Article - Journal
© 1993 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.