Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance

In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is furthe...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2018-01, Vol.15 (1), p.151-155
Main Authors: Ratha, Debanshu, Bhattacharya, Avik, Frery, Alejandro C.
Format: Article
Language:English
Subjects:
Classification
Data
Data acquisition
Distance
geodesic distance
Level measurement
Matrix decomposition
Methods
Orientation
polarimetry
Power measurement
Radar
Radar data
Radar polarimetry
Radar scattering
SAR (radar)
Scattering
Similarity
similarity measure
Similarity measures
Symmetric matrices
Synthetic aperture radar
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Summary:In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al. based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman-Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2017.2778749