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The polarimetric distortions present in the polarimetric SAR (PolSAR) data sets leads to unexpected results from the polarimetric decomposition and classification models, which in turn result in the ground target misinterpretation. The most important polarimetric distortions in airborne PolSAR data sets 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 and 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 data set was not completely analyzed and also the temporal variability of the polarimetric distortions using multiple data sets 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 Uninhibited Aerial Vehicle Synthetic Aperture Radar 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 data sets to assess the spatio‐temporal behavior of these polarimetric distortions. The polarimetric signatures and the coherency matrix behavior of the trihedral corner reflectors were used to analyze 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 and the channel imbalance and phase bias present in the data sets were not high. From the spatio‐temporal analysis, it was found that the variability of these polarimetric distortions both in spatial and temporal domains was minimum and can be discarded without causing any considerable error in the polarimetric calibration process. Key Points: The major cause for the polarimetric distortions in the UAVSAR data sets was due to the crosstalkThe channel imbalance and phase bias distortions were not high for all the data setsPolSAR calibration involves the use of Quegan, Improved Quegan, and Ainsworth methods in this study [ABSTRACT FROM AUTHOR]

The Polarimetric Synthetic Aperture Radar (PolSAR) systems use electromagnetic radiations of different polarizations in the microwave frequency to collect the scattering information from targets on the Earth. Nevertheless, as with any other electronic device, the PolSAR systems are also not ideal and subjected to distortions. The most important of these distortions are the polarimetric distortions caused due to the channel imbalance, phase bias, and crosstalk between the different polarization channels. For the spaceborne PolSAR systems, the Earth's ionosphere also contributes to an additional polarimetric distortion known as the Faraday rotation. An effort was made in this study to perform the polarimetric calibration of the Quad-pol and Compact-pol PolSAR datasets acquired using different airborne and spaceborne PolSAR systems to estimate and minimize these polarimetric distortions. The investigation was also done to analyze the impact of these polarimetric distortions on the scattering mechanisms from ground targets and on its dependency on the radar wavelength. The study was done using the UAVSAR L-band Quad-pol dataset, RADARSAT-2 Quad-pol dataset, ALOS-2 PALSAR-2, ISRO's L&S- Band Airborne SAR (LS-ASAR) Quad-pol and Compact-pol datasets, and the RISAT-1 Compact-pol dataset. Calibration of the airborne PolSAR data was carried to understand the level of polarimetric distortions in the LS-ASAR product that is a precursor mission to the spaceborne Dual-Frequency L&S Band NASA-ISRO Synthetic Aperture Radar (NISAR) mission. It is understood that the crosstalk is the dominant polarimetric distortion, which severely affects the PolSAR datasets compared to the other polarimetric distortions, and it is more for the higher wavelength PolSAR systems. The Quegan, Improved Quegan, and Ainsworth algorithms for crosstalk estimation and minimization was performed for the different Quad-pol datasets and it was found that the Improved Quegan algorithm is suitable for removing crosstalk from datasets having high crosstalk and the Ainsworth algorithm is suitable for removing crosstalk from datasets having low crosstalk. The Freeman method of the polarimetric calibration was implemented for the compact-pol datasets and it was able to considerably minimize the polarimetric distortions. The coherency matrix, scattering matrix, model-based decomposition, polarimetric signatures, and roll invariant parameter-based analysis revealed that all the datasets after polarimetric calibration were showing the correct scattering responses expected from the ground targets. [ABSTRACT FROM AUTHOR]

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 minimize 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 study, 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 polarization 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. Key Points: A computationally efficient algorithm for Polarimetric Synthetic Aperture Radar (PolSAR) calibrationEstimation and comparison of PolSAR data quality before and after the calibrationComparison of existing PolSAR calibration algorithms [ABSTRACT FROM AUTHOR]

India's radar imaging satellite RISAT-1 was the first spaceborne SAR platform operated in the compact polarimetric (CP) mode, which posed several advantages compared to the commonly used quad-pol system architecture in terms of design complexity and data quality. But, to make use of the advantages offered by the compact polarimetry mode, the acquired datasets should be free from the system induced polarimetric distortions, which include the channel imbalances, phase bias, and crosstalk between the channels. In addition to these system induced errors, the ionosphere also induces polarimetric distortion to the datasets in terms of Faraday rotation. For accurate ground target characterization using the CP datasets, the polarimetric distortions should be estimated and minimized. This article focuses on the CP calibration of the RISAT-1 dataset using trihedral and dihedral corner reflectors deployed at the study area. The Cloude CP decomposition technique is used to assess the ground target characterization quality of the dataset before and after polarimetric calibration. [ABSTRACT FROM AUTHOR]