Unsupervised segmentation of polarimetric SAR data using the covariance matrix
نویسندگان
چکیده
منابع مشابه
Unsupervised segmentation of polarimetric SAR data using the covariance matrix
This paper presents a method for unsupervised segmentation of polarimetric synthetic aperture radar (SAR) data into classes of homogeneous microwave polarimetric backscatter characteristics. Classes of polarimetric backscatter are selected based on a multidimensional fuzzy clustering of the logarithm of the parameters composing the polarimetric covariance matrix. The clustering procedure uses b...
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Abstract—In this paper we show that the eigen-decomposition of the covariance matrix can be used to assess the quality of polarimetric SAR data. The fourth eigenvalue, λ4, is a measure for the noise in the image, independent of image calibration. Misregistration of the two cross-polarization channels leads to image content appearing in the λ4 image and is easily detectable. No sensitivity to cr...
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This paper introduces an approach to the classification and interpretation of SAR data using complementary polarimetric and interferometric information. Strictly polarimetric and polarimetric interferometric data are first analyzed and classified separately. An unsupervised polarimetric segmentation, based on multivariate Wishart statistics, is applied to one of the separate polarimetric datase...
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One of the most complex problems in polarimetric radar imaging is how to interpret the measured covariance matrix in terms of known scattering mechanisms. Two main approaches are commonly used for this purpose. The first, introduced by Cloude [1] involves calculating the eigenvectors of the measured covariance matrix. This method provides a unique result. However, as shown by van Zyl [2], when ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 1992
ISSN: 0196-2892
DOI: 10.1109/36.158863