NDLI: Classification of multi-look polarimetric SAR data based on complex Wishart distribution

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Proceedings of IEEE National Telesystems Conference - NTC '94

Conference Proceedings National Telesystems Conference 1993

[Proceedings] NTC-92: National Telesystems Conference

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Classification of multi-look polarimetric SAR data based on complex Wishart distribution

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NTC '91 - National Telesystems Conference Proceedings

Classification of multi-look polarimetric SAR data based on complex Wishart distribution

Content Provider	IEEE Xplore Digital Library
Author	Lee, J.S. Grunes, M.R.
Copyright Year	1992
Description	Author affiliation: US Naval Res. Lab., Washington, DC, USA (Lee, J.S.; Grunes, M.R.)
Abstract	An optimal feature classification scheme is developed for multilook polarimetric SAR (synthetic aperture radar) imagery based on a multivariate complex Wishart distribution. The purpose is to identify various ground covers, such as forest, vegetation, city block, ocean, and sea ice type. Multilook polarimetric SAR data can be represented either in Stoke's matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A simple but effective classifier is then developed using the complete information of the complex covariance. This algorithm is further extended to classification using multifrequency polarimetric data. A procedure for assessing the classification errors is also developed using a Monte Carlo simulation. The effectiveness of this algorithm is demonstrated with NASA/JPL (Jet Propulsion Laboratory) P-, L-, and C-band polarimetric SAR data.
File Size	339230
File Format	PDF
ISBN	078030554X
DOI	10.1109/NTC.1992.267879
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-05-19
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrix Sea ice NASA Gaussian distribution Vegetation mapping Cities and towns Oceans Layout Radar scattering Speckle
Content Type	Text
Resource Type	Article

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