A new statistical model for Markovian classification of urban areas in high-resolution SAR images

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classificatio...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2004-10, Vol.42 (10), p.2046-2057
Main Authors: Tison, C., Nicolas, J.-M., Tupin, F., Maitre, H.
Format: Article
Language:English
Subjects:
Classification algorithms
Clutter
Computer Science
Context modeling
Image Processing
Image resolution
Mathematical models
Radar scattering
Statistical distributions
Statistics
Studies
Surface fitting
Synthetic aperture radar
Urban areas
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Summary:We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, Nakagami-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolution. The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground, buildings, and vegetation.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2004.834630