Detecting covariance symmetries for classification of polarimetric SAR images

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Pallotta, Luca and Clemente, Carmine and De Maio, Antonio and Soraghan, John J. (2017) Detecting covariance symmetries for classification of polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 55 (1). pp. 80-95. ISSN 0196-2892 (https://doi.org/10.1109/TGRS.2016.2595626)

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Abstract

The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, coherence matrix, Muller matrix, and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric SAR images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image.

ORCID iDs

Pallotta, Luca, Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X, De Maio, Antonio and Soraghan, John J. ORCID logoORCID: https://orcid.org/0000-0003-4418-7391;
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