Paper
21 November 1995 Clutter discrimination in polarimetric SAR imagery
David Blacknell, Robert J.A. Tough
Author Affiliations +
Abstract
Polarimetric SAR images will contain extended regions of apparently homogeneous clutter arising, perhaps, from areas of vegetation or uniformly driven expanses of water. Such regions may contain localized clutter variations which indicate the presence of features of interest such as changes in vegetation density due to environmental effects, damping of the sea surface due to the presence of pollutants or the effects of partially concealed land-based or maritime military targets. It is thus of interest to develop techniques which can discriminate localized clutter features from the background clutter. If the distribution parameter values are known for both background and feature then the likelihood ratio method can be used. Frequently, however, the feature parameter values are unknown in which case one option is to use simply the background likelihood. In both cases, quadratic test statistics result which are analyzed to allow a comparison of theoretical performances. A second option is to introduce a probability distribution for the feature parameter values. Optimum performance will result if the assumed and true distributions match exactly but any mismatch may considerably reduce the performance. Simulations are used to investigate this effect. In conclusion, the paper assesses the various discrimination techniques in terms of complexity, prior knowledge requirements and performance.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Blacknell and Robert J.A. Tough "Clutter discrimination in polarimetric SAR imagery", Proc. SPIE 2584, Synthetic Aperture Radar and Passive Microwave Sensing, (21 November 1995); https://doi.org/10.1117/12.227127
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Polarimetry

Synthetic aperture radar

Statistical analysis

Solids

Vegetation

Computer simulations

Data modeling

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