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.
S. Ghaloum et al., "Terrain Classification in SAR Images Using Principal Components Analysis and Neural Networks," IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers (IEEE), Jan 1993.
The definitive version is available at http://dx.doi.org/10.1109/36.214928
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)
Article - Journal
© 1993 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.