Decomposition Theorems Applied to Random and Stationary Radar Targets

Unal, C.M.H.; Ligthart, L.P.

doi:10.2528/pier97040700

Cited by 6 publications

(4 citation statements)

References 6 publications

(17 reference statements)

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“…K 3 contains the difference between even-and odd-bounce scattering which play a key role in the Pauli decomposition as well as in the coherency matrix and all further decompositions (Unal and Ligthart, 1998;Alberga, 2004). K 7 holds the inter-channel correlation showing the phase difference between the odd-and evenbounce scattering event.…”

Section: Dual-pol Imagesmentioning

confidence: 99%

The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation

Schmitt

Wendleder

Hinz

2015

ISPRS Journal of Photogrammetry and Remote Sensing

111

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a b s t r a c tAs the number of space-borne SAR sensors increases, a rising number of different SAR acquisition modes is in use, resulting in a higher variation within the image products. This variability in acquisition geometry, radiometry, and last but not least polarimetry raises the need for a consistent SAR image description incorporating all available sensors and acquisition modes. This paper therefore introduces the framework of the Kennaugh elements to comparably represent all kinds of multi-scale, multi-temporal, multi-polarized, multi-frequency, and hence, multi-sensor data in a consistent mathematical framework. Furthermore, a novel noise model is introduced that estimates the significance and thus the (polarimetric) information content of the Kennaugh elements. This facilitates an advanced filtering approach, called multi-scale multi-looking, which is shown to improve the radiometric accuracy while preserving the geometric resolution of SAR images. The proposed methodology is finally demonstrated using sample applications that include TerraSAR-X (X-band), Envisat-ASAR, RADARSAT-2 (C-band) and ALOS-PALSAR (L-band) data as well as the combination of all three frequencies. Thus the suitability of the Kennaugh element framework for practical use in proved for advanced SAR remote sensing.

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Section: Dual-pol Imagesmentioning

confidence: 99%

The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation

Schmitt

Wendleder

Hinz

2015

ISPRS Journal of Photogrammetry and Remote Sensing

111

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“…According to the eigendecomposition theorem, the 3x3 coherency matrix 3 can be represented as follows [10]:…”

Section: A Sar Polarimetry Based Approachmentioning

confidence: 99%

Road Surface Roughness Estimation Using Polarimetric SAR Data

Babu

Baumgartner

2020

2020 21st International Radar Symposium (IRS)

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The road surface roughness directly influences the grip and skid resistance of the vehicles. Since these parameters are relevant for the safety of the road users, they have to be continuously monitored to keep track of its changes. The potential of airborne polarimetric SAR to remotely monitor the road surface roughness is investigated in this study using fully polarimetric X-band data acquired with DLR's airborne radar sensor F-SAR. The polarimetric analysis revealed that the anisotropy and coherency matrix (T3) elements are sensitive to the road surface roughness. Additionally SAR backscatter based empirical models for surface roughness estimation were investigated.

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“…In this study, the polarimetric scattering anisotropy generated from the eigenvalues of the coherency matrix 3 and the elements of the 3 matrix itself is considered to generate the parameter. According to the eigen-decomposition theorem, the 3x3 coherency matrix 3 can be represented as follows [13]:…”

Section: A Road Surface Roughness Estimationmentioning

confidence: 99%

Road Surface Quality Assessment Using Polarimetric Airborne SAR

Babu

Baumgartner

2020

2020 IEEE Radar Conference (RadarConf20)

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Since the road infrastructure is an important factor for the economy and safety, the continuous monitoring of the road condition is a necessity. Compared to the widely used costly, time consuming and labour-intensive road condition monitoring using measurement vehicles all over the country, the potential of SAR polarimetry to remotely monitor the road surface roughness, cracks and potholes are investigated in this study. The polarimetric analysis of fully polarimetric X-band radar datasets acquired over the Kaufbeuren test site with DLR's airborne sensor F-SAR revealed that the anisotropy and coherency matrix (T3) elements are sensitive to the road surface roughness and can be used to retrieve the vertical surface roughness. The crosspolar sigma nought images show a considerable increase in their magnitude over the cracks and potholes on the road surface. The initial experimental results obtained from this study are discussed in this paper.

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Decomposition Theorems Applied to Random and Stationary Radar Targets

Cited by 6 publications

References 6 publications

The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation

The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation

Road Surface Roughness Estimation Using Polarimetric SAR Data

Road Surface Quality Assessment Using Polarimetric Airborne SAR

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