High resolution dual-channel SAR-system for airborne applications

Caris, M.; Brehm, Thorsten; Palm, Stephan; Sieger, Stefan; Klöppel, Frank; Sommer, Rainer; Janssen, Daniel; Port, V.; Stanko, Stephan

doi:10.23919/irs.2017.8008093

Cited by 4 publications

(1 citation statement)

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“…P OLARIMETRIC interferometry is a recent technique, [1] which uses two spatially shifted polarimetric antennas allowing the measurement of the coherence associated with the various polarimetric channels as well as the height of the polarimetric phase centers. The potential of this technique for the extraction of physical parameters from vegetation, ice, and urban areas has been validated on various data sets and is still being evaluated on other types of data acquired at high resolution by airborne systems [2]- [4]. The polarimetric scattering phenomenon of a medium can be described completely using a (3 × 3) matrix formulation, namely, the covariance matrix [5].…”

Section: Introductionmentioning

confidence: 99%

Covariance Symmetries Detection in PolInSAR Data

Tahraoui

Clemente

Pallotta

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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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.

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