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

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 o...

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Published inIEEE transactions on geoscience and remote sensing Vol. 31; no. 2; pp. 511 - 515
Main Authors Azimi-Sadjadi, M.R., Ghaloum, S., Zoughi, R.
Format Journal Article
LanguageEnglish
Published IEEE 01.03.1993
Subjects
Fourier transforms
Geometry
Intelligent networks
Neural networks
Polarization
Position measurement
Power engineering computing
Principal component analysis
Radar scattering
Tomography
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Summary: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.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0196-2892
1558-0644
DOI:10.1109/36.214928