Covariance symmetries detection in PolInSAR data

Sofiane Tahraoui, Carmine Clemente, Luca Pallotta, John J. Soraghan, Mounira Ouarzeddine

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach.
LanguageEnglish
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date11 Jul 2018
DOIs
Publication statusE-pub ahead of print - 11 Jul 2018

Fingerprint

Polarimeters
Synthetic aperture radar
interferometry
Interferometry
symmetry
synthetic aperture radar
Image classification
Covariance matrix
Remote sensing
Moisture
Textures
Surface roughness
image classification
detection
Availability
roughness
moisture content
texture
remote sensing
matrix

Keywords

  • Azimuth symmetry
  • cross covariance
  • detection
  • polarimetric interferometry
  • PolInSAR
  • reflection symmetry
  • rotation symmetry
  • symmetries

Cite this

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title = "Covariance symmetries detection in PolInSAR data",
abstract = "In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach.",
keywords = "Azimuth symmetry, cross covariance, detection, polarimetric interferometry, PolInSAR, reflection symmetry, rotation symmetry, symmetries",
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Covariance symmetries detection in PolInSAR data. / Tahraoui, Sofiane; Clemente, Carmine; Pallotta, Luca; Soraghan, John J.; Ouarzeddine, Mounira.

In: IEEE Transactions on Geoscience and Remote Sensing, 11.07.2018.

Research output: Contribution to journalArticle

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AU - Tahraoui, Sofiane

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AU - Pallotta, Luca

AU - Soraghan, John J.

AU - Ouarzeddine, Mounira

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