Polarimetric Calibration of RISAT-1 Compact-Pol Data

Babu, Arun; Kumar, Shashi; Agrawal, Shefali

doi:10.1109/jstars.2019.2932019

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…On the other hand, according to (8), the calibrated SAR data and the SAR data to be calibrated can be expressed as σ vh1 = 0.095(0.13 + sin 1.5θ 1 ) 1.4 (1 − exp(−1.3(ks) 0.9 ))σ 0 vv1 (10) σ vh2 = 0.095(0.13 + sin 1.5θ 1 ) 1.4 (1 − exp(−1.3(ks) 0.9 ))σ 0 vv2 (11) By dividing (10) with (11) and combining (9), we can obtain Rewriting (12), the relationship between the calibrated SAR data and the SAR data to be calibrated in VV polarization can be expressed as σ vv1 = cos(θ 1 ) 2.2 (0.13 + sin 1.5θ 2 ) 1.4 cos(θ 2 ) 2.2 (0.13 + sin 1.5θ 1 ) 1.4 σ vv2 (13) The derived backscattering coefficients in ( 9) and ( 13) can then be used to calibrate the SAR satellite data to be calibrated.…”

Section: Correction Of Imaging Parameter Differencesmentioning

confidence: 99%

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Synthetic Aperture Radar Radiometric Cross Calibration Based on Distributed Targets

Zhou

Duan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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SAR radiometric calibration is an essential step in inferring meaningful physical information about the target. The majority of current calibration processes are directly based on artificial calibrators, but the number of calibrators is always restricted. Cross calibration, which has been widely used for optical and meteorological satellites, performs calibration by another calibrated satellite. Distributed targets are used as the calibration reference, allowing for frequent calibration. However, the potential of introducing cross calibration to SAR satellites has not been confirmed, and two issues must be resolved: 1) The number of calibration targets available for cross calibration is limited to a few known distributed targets; 2) The imaging parameters of calibrated and uncalibrated SARs are different, which may cause errors when cross calibration is applied. In this paper, a method for selecting stable distributed targets using time-series stability analysis was presented firstly. The salinealkali land and urban areas were selected as the calibration targets with low and high backscattering, respectively. Second, different stable pixel extraction methods were adopt based on the characteristics of different targets. Thirdly, the Oh model was used to correct the scattering difference caused by the incidence angle difference, which greatly improved the accuracy of the backscattering coefficients of the calibration targets. The cross calibration experiments on the same series of satellite (Sentinel-1A/B) data revealed that the accuracy of cross calibration was comparable to the accuracy of the artificial calibrator-based method, with a difference of less than 0.48 dB (1σ).

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Section: Correction Of Imaging Parameter Differencesmentioning

confidence: 99%

“…12) This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/…”

mentioning

confidence: 99%

Synthetic Aperture Radar Radiometric Cross Calibration Based on Distributed Targets

Zhou

Duan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Different polarimetric decomposition and classification techniques are widely used to extact information from the PolSAR data sets (Kumar et al., 2020; Ullmann et al., 2016), but the polarimetric distortions present in the PolSAR data sets leads to wrong results from the polarimetric decomposition and classification models (Kumar et al., 2021), which in turn result in the ground target misinterpretation (Jung & Park, 2018; Maiti et al., 2021). The most important of the polarimetric distortions is the channel imbalance, crosstalk, and Faraday rotation errors (Babu et al., 2019a). The channel imbalance between the different polarizations occurs mainly due to the gain mismatch between the power amplifiers used for horizontal and vertical transmission of the electromagnetic waves.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Calibration and Spatio‐Temporal Polarimetric Distortion Analysis of UAVSAR PolSAR Data

Babu

Kumar

Agrawal

2022

Earth and Space Science

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The polarimetric distortions present in the PolSAR datasets leads to unexpected results from the polarimetric decomposition and classification models which in turn results in the ground target misinterpretation. The most important polarimetric distortions in airborne PolSAR datasets are the channel imbalance and crosstalk. The minimization of these polarimetric distortions using polarimetric calibration techniques is very important to carry out the quantitative analysis using PolSAR data, time series analysis and to compare results between different sensors. In the current state of the art, the spatial variability of the polarimetric distortions at different slant-range positions in the dataset were not completely analysed and also the temporal variability of the polarimetric distortions using multiple datasets needs to be more investigated. Therefore, the main objectives of this study were to develop an operational processor to perform the external polarimetric calibration of the L-band UAVSAR system and to analyze the spatio-temporal behavior of the polarimetric distortions. The Polarimetric Distortion Matrix (PDM) was formulated after considering the crosstalk and channel imbalance polarimetric distortions to derive the scattering matrix free from all kinds of polarimetric distortions from the observed scattering matrix. The Quegan, improved Quegan and Ainsworth methods for polarimetric calibration were implemented to solve the PDM. The residual channel imbalance and crosstalk after polarimetric calibration were estimated to compare the calibration efficiency of these methods. After that, a spatio-temporal analysis was carried out with several datasets to assess the spatio-temporal behavior of these polarimetric distortions. The polarimetric signatures and the coherency Matrix behaviour of the trihedral corner reflectors were used to analyse the ground target characterization improvement after polarimetric calibration. The results obtained from this study revealed that the crosstalk is the major cause for the polarimetric distortions, the channel imbalance and phase bias present in the datasets were not high. From the spatio-temporal analysis, it was found that the variability of these polarimetric distortions both in spatial and temporal domains were minimum and can be discarded without causing any considerable error in the polarimetric calibration process.

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“…These errors relatively contribute to an increased magnitude of cross‐pol images resulting in overestimation of volume scattering properties (Chang et al., 2018). In addition, the amplitude or phase unconformity of different polarization channels cause imbalance in the transmission or reception of antenna gain (Babu et al., 2019). Coming to the influence of imbalance on the PolSAR images, there are two categories: (a) co‐pol channel imbalance and (b) cross‐pol channel imbalance.…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient Hybrid Framework for Polarimetric Calibration of Quad‐Pol SAR Data

Maiti

Kumar

Tolpekin³

et al. 2021

Earth and Space Science

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Polarimetric Synthetic Aperture Radar (PolSAR) calibration is an essential preprocessing step that must be performed to ensure that the data quality is adequate. This, in turn, helps to minimise the propagation of errors in any further data processing or information extraction. Crosstalk and channel imbalance are two major distortions found to be present in the uncalibrated polarimetric SAR data. The PolSAR calibration mainly aims to reduce these two distortions revealing the true scattering pattern of the targets. In this regard, Quegan's algorithm and Ainsworth algorithm are two widely used algorithms for the PolSAR calibration. In this research, the accuracy and efficiency of these two algorithms have been thoroughly compared using suitable metrics. It has been shown that the Ainsworth algorithm performs better than Quegan's in terms of accuracy at the cost of poor computational efficiency. The data quality metrics also highlight the better calibration accuracy of the Ainsworth algorithm. The issue of higher computational complexity has been effectively addressed by coupling both of these algorithms. Evidently, the computational cost has been reduced in the case of the proposed algorithm. The polarisation orientation angle (POA) shift is another distortion caused by the topographic variations present in the target scene. Therefore, correction of POA shift has been incorporated in this research by coupling it with the PolSAR calibration. Subsequently, the improvement in the scattering has been observed. In essence, the proposed algorithm coupled with the correction of POA shift rectifies the major polarimetric distortions with adequate accuracy and computational efficiency.

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Polarimetric Calibration of RISAT-1 Compact-Pol Data

Cited by 14 publications

References 21 publications

Synthetic Aperture Radar Radiometric Cross Calibration Based on Distributed Targets

Synthetic Aperture Radar Radiometric Cross Calibration Based on Distributed Targets

Polarimetric Calibration and Spatio‐Temporal Polarimetric Distortion Analysis of UAVSAR PolSAR Data

A Computationally Efficient Hybrid Framework for Polarimetric Calibration of Quad‐Pol SAR Data

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