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The polarimetric Synthetic Aperture Radar (SAR) data sets have been widely exploited for land use land cover (LULC) classification due to their sensitivity to the structural and dielectric properties of the imaging target. In this study, the potential of fully polarimetric L‐ and S‐band Airborne SAR (LS‐ASAR) data sets were explored for the machine‐learning‐based classification of Urban, Vegetation, Waterbody, and Open Ground. This work was done by utilizing dual‐frequency L‐ and S‐band airborne data of Santa Barbara, California, USA, acquired under the Airborne SAR (LS ASAR) campaign, a precursor airborne mission to the space‐borne NASA‐ISRO (NISAR) mission. The LS‐ASAR polarimetric information was utilized for LULC classification using the SVM classifier. The roll‐invariant Barnes, and eigenvalue/eigenvector‐based Cloude and H/A/Alpha decomposition were implemented to retrieve the scattering parameters. The backscatter response of classes was studied, and separability analysis was done to reduce the misclassification error between six class pairs‐ Vegetation—Urban, Vegetation—Waterbody, Vegetation—Open Ground, Urban—Waterbody, Urban—Open Ground, and Water—Open Ground. The decomposition models failed to achieve the desirable separability index for all six class pairs; consequently, the classification of Barnes, Cloude, H/A/Alpha decomposition showed misclassification between vegetation‐urban class, and waterbody‐open ground class for both L‐ and S‐band data sets. The effort was made toward improving the classification accuracy by integrating the roll‐invariant and eigenvalue‐eigenvector scattering parameters of the multifrequency L‐ and S‐band data set. This method presented the desirable separability index for all class‐pair; eventually highest classification accuracy was achieved i.e. 93.35% (kc ${k}_{c}$ = 0.91) by significantly reducing the misclassification error between class pairs. Key Points: Implementation of Barnes, Cloude & H/A/Alpha polarimetric decomposition models on dual‐frequency polarimetry SAR L‐ and S‐band data setsSupport Vector Machine classification of scattering parameters and generation of confusion matrices for Accuracy AssessmentImproving the classification accuracy by integrating the polarimetric scattering components of L‐ and S‐band data sets [ABSTRACT FROM AUTHOR]

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]

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]

Polarimetric synthetic‐aperture radar (SAR) remote sensing has been widely used for structural and biophysical parameters retrieval of forest vegetation. It has been found that the combination of polarimetric properties and interferometric characteristics of SAR remote sensing provides the capacity to retrieve forest height. The prime objective of this research was to investigate the potential of Polarimetric Synthetic Aperture Radar Tomography (PolTomSAR) for the forested and river region of Mondah National Park, Gabon. SAR tomography is an improved method for acquiring the height of geographical features. UAVSAR L‐band fully polarimetric multibaseline data have been used in this research (1.275 GHz). SAR data and ground data over the area have been collected in the year 2016. With the superresolution‐based Capon algorithm, multiple scatterers located at a different vertical position in the same azimuth range cell has been resolved and reconstructed. This work provides a framework for Capon‐based tomographic processing of multibaseline UAVSAR data for vertical profile retrieval of forest vegetation. The height profile of the forest patch having sparse, as well as dense vegetation, were retrieved. The vertical profile for a single azimuthal bin was obtained in range direction. The tomographic profile obtained was cross‐checked with the field‐measured forest height for the 16 locations in Mondah National Park, Gabon. To check the accuracy of the applied method, the statistical method of R2 and root‐mean‐square error (RMSE) is employed. The obtained RMSE of the result is 4.21 m and R2 is 0.92. The obtained results were concluded to find the potential of the Capon algorithm for the tomographic reconstruction of UAVSAR data. Key Points: PolSAR Tomography of UAVSAR data for forest height retrievalAn analysis of tomographic profiles for forest vegetation and water bodyAccuracy assessment of PolSAR tomography‐based forest height retrieval [ABSTRACT FROM AUTHOR]

Target characterization is an essential aspect of polarimetric decomposition. This technique is capable of categorizing polarimetric signatures for different types of targets based on the scattering mechanisms they follow, enabling straightforward physical interpretation of the targets. The geometric anomalies associated with human‐made targets escalate the degree of randomness in the scattering process, which causes scattering ambiguity for such targets. The second‐order model descriptors do not relate to the actual physical structure and yield predominant volume scattering power. Such urban targets are decomposed as natural targets leading to irrelevant decomposition results. The methods developed to curb the problem are unable to maintain the consistency in the decomposition modeling as they underestimate volume scattering powers for natural landcover. A hybrid decomposition model is proposed herein to solve the problem of predominant volume scattering observed from urban targets by preserving volume scattering powers for natural targets. The model uses eigenvalue‐based decomposition parameters and polarimetric interferometric synthetic aperture radar (PolInSAR) coherence to decompose ambiguous targets. The proposed model has been tested on NISAR UAVSAR PolInSAR data acquired over the Greenville region, MS, USA. The proposed model has increased the double‐bounce scattering from the urban targets and enhanced the volume scattering from natural landcover as well. By comparing the results with existing decomposition models, it is observed that the proposed model gives a more robust representation of the landcover than the compared decomposition models. Plain Language Summary: The SAR sensor illuminates the surface with polarized microwaves and receives the interacted backscattered wave. The backscattered wave is transformed into information, which is represented by a scattering matrix. Due to different geophysical properties of the targets, there is a change in the backscattered wave. Polarimetric SAR decomposition is a technique that can relate the change in backscatter to the physical structure of the target, enabling simple and robust classification of the landcover. The techniques decompose or disintegrate the scattering matrix into scattering mechanisms, and each scattering mechanism represents a broad category of different landcover. Owning to the random nature of real‐world observations, the backscatter for two different categories of landcover can be the same, such as a building and a tree. Therefore, for such cases, the polarimetric decomposition yields inaccurate classification results, and from the polarimetric point of view, this problem is referred to as overestimation of volume scattering. The reason for this is the assumptions and the design of the model. Various modifications have been proposed to solve the problem of overestimation. We find that direct modification may yield inconsistent decomposition results, which can be critical for different utilities of polarimetric decompositions. We propose a new model that associates SAR interferometry with polarimetric decompositions to solve the problem of predominant volume scattering. The new model has been tested on L‐band simulated NISAR UAVSAR data acquired over the Greenville region, MS, USA. Key Points: PolInSAR coherence to characterize targets as permanent or dynamic featuresEntropy describes the randomness of the scattering processModel‐based decompositions have been proposed for the scattering‐based characterization of manmade and natural features [ABSTRACT FROM AUTHOR]

The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm−2) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination (R2) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm−2) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm−2) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables. Key Points: Important polarimetric observables and their sensitivity toward biophysical parameters were analyzedEffective retrieval of crop parameters from a single‐date polarimetric data using Random Forest and Multiple Linear Regression was explored [ABSTRACT FROM AUTHOR]