Three-component Decomposition Research Articles

A general three-component polarimetric SAR interferometry target decomposition

In this study, a general three-component polarimetric SAR interferometry (PolInSAR) target decomposition framework is proposed by modifying the existing generalized surface, double-bounce, and volume models. The resulting general models are then compared to the original Freeman-Durden modeling strategy. Three types of generalized volume scattering models (generalized volume scattering model (GVSM), simplified Neumann volume scattering model (SNVSM) and simplified adaptive volume scattering model (SAVSM)) were employed. Simulated L-band PolInSAR data over deciduous and pine forest stands generated by PolSARpro and DLR P-band airborne PolInSAR data over a tropical forest area from the AfriSAR 2016 campaign were used for performance analysis. A qualitative comparison of the decomposition results shows that the three generalized volume scattering models generally deviate from the Freeman-Durden model, showing that the GVSM and SNVSM models have very similar results. In the case of airborne data over tropical forests, a tomographic synthetic aperture radar (TomoSAR) profile was also computed and used as a benchmark for comparison with the phase-center profiles of all four volume-scattering components. Not only do the GVSM and SNVSM models exhibit similar results between them (as with simulated data), but also a better match with the HV TomoSAR profile.

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observations from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three-component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China. The results showed that T11, T33, and total backscattered power (SPAN) are highly correlated with forest canopy height. In addition, this study showed that determining the weights of different polarization observation variables contributes positively to the accurate estimation of forest canopy height. The forest canopy height-weighted inversion method proposed in this paper was shown to be superior to the multiple regression model, with a 26% improvement in r and a 0.88 m reduction in the root-mean-square error (RMSE).

Thresholding of Polarimetric SAR Images of Coastal Zones Based on Three-Component Decomposition and Likelihood Ratio

Thresholding of Polarimetric SAR Images of Coastal Zones Based on Three-Component Decomposition and Likelihood Ratio

An Optimal Polarization SAR Three-Component Target Decomposition Based on Semi-Definite Programming

The model-based polarimetric synthetic aperture radar (PolSAR) target decomposition decodes the scattering mechanism of the target by analyzing the essential scattering components. This paper presents a new general three-component scattering power decomposition method by establishing optimization problems. It is known that the existing three-component decomposition method prioritizes the contribution of volume scattering, which often leads to volume scattering energy overestimation and may make double-bounce scattering and odd-bounce scattering component power negative. In this paper, a full parameter optimization method based on the remainder matrix is proposed, where all the elements of the coherency matrix will be taken into account including the remaining T13 component. The optimization is achieved with no priority order by solving the problem using semi-definite programming (SDP) based on the Schur complement theory. By doing so, the problem of volume scattering energy overestimation and negative powers will be avoided. The performance of the proposed approach is demonstrated and evaluated with AIRSAR and GF-3 PolSAR data sets. The experimental results show that by using the proposed method, the power contributions of volume scattering in two sets of data were reduced by at least 2.6% and 3.7% respectively, compared to traditional methods. And the appearance of negative power of double-bounce scattering and odd-bounce scattering are also avoided compared with those of the existing three-component decomposition.

Morphological Decomposition of the Early-type Spiral Galaxy M81 (NGC 3031)

This paper presents a morphological investigation of the early-type spiral galaxy M81 (NGC 3031) through decomposition by fitting radial profiles of surface brightness using the software GALFIT, aimed at exploring structural components of M81 and quantifying their morphology. In this work, we adopt 6 types of decompositions with different numbers of morphological components, among which the most complicated one contains 5 components such as a bulge, a disk, a pair of outer spiral arms, a pair of inner spiral arms, and a galactic nucleus. The results show that, M81 hosts a classical bulge with the Sérsic index ∼ 5.0; the morphology and the luminosity for the bulge are almost constant in the different decomposition types. The disk of M81 has the Sérsic index ∼ 1.2 but the morphology and the luminosity are found sensitive to the inclusion of the inner spiral arms in the decomposition or not. The results of this work indicate that the combination of individual substructures has a considerable impact on the morphology of the galaxy as a mixture. On the basis of the results, the usability of the different types of decomposition is suggested in this work. The three-component decomposition, i.e., bulge + disk + nucleus, is applicable to statistics of large samples of galaxies; more complicated decomposition with spiral arms taken into account is suitable for precise measurements of individual galaxies in small samples. This work is based on the single-band Spitzer-The Infrared Array Camera (IRAC) 4.5 μm image. In the future, we will carry out multi-wavelength decomposition, for the purpose of investigating spectral energy distributions and stellar population properties for the galactic substructures, and thereby deduce their formation history and evolution processes.

PolSAR Image Classification by Introducing POA and HA Variances

A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to describe the difference between forest scattering and building scattering. However, this problem cannot effectively be solved with scattering power alone since HV polarization records significant scattering powers from building areas that are similar to vegetation. Therefore, in this study, two new parameters, the polarization orientation angle (POA) variance and helix angle (HA) variance, were defined to describe the distributions of buildings and forests. By combining scattering power with POA and HA variances, the random forest algorithm was used to conduct the land cover classification, focusing on distinguishing between forests and oriented buildings. Finally, the C- and L-band polarimetric SAR data acquired by the GF-3, ALOS1 PALSAR, and SAOCOM systems were selected to test the proposed method. The results indicate that it is feasible to improve PolSAR classification accuracy by introducing polarimetric parameters. Quantitatively, the classification accuracies increased by 23.78%, 10.80%, and 12.97% for the ALOS1 PALSAR, GF-3, and SAOCOM data, respectively.

Improving the Potential of Coniferous Forest Aboveground Biomass Estimation by Integrating C- and L-Band SAR Data with Feature Selection and Non-Parametric Model

Forests play a significant role in terrestrial ecosystems by sequestering carbon, and forest biomass is a crucial indicator of carbon storage potential. However, the single-frequency SAR estimation of forest biomass often leads to saturation issues. This research aims to improve the potential for estimating forest aboveground biomass (AGB) by feature selection based on a scattering mechanism and sensitivity analysis and utilizing a non-parametric model that combines the advantage of dual-frequency SAR data. By employing GF-3 and ALOS-2 data, this study explores the scattering mechanism within a coniferous forest by using results of target decomposition and the pixel statistics method. By selecting an appropriate feature (backscatter coefficients and polarization parameters) and using stepwise regression models and a non-parametric model (the random forest adaptive genetic algorithm (RF-AGA)), the results revealed that the RF-AGA model with feature selection exhibited excellent AGB estimation performance without obvious saturation (RMSE = 10.42 t/ha, R2 = 0.93, leave-one-out cross validation). The σHV, σVH, Pauli three-component decomposition, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of thee C-band and σHV, σVH,σHH, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of the L-band are suited for estimating the AGB of coniferous forests. Volume scattering was the dominant mechanism, followed by surface scattering, while double-bounce scattering had the smallest proportion. This study highlights the potential of investigating scattering mechanisms, sensitivity factors, and parameter selection in the C- and L-band SAR data for improved forest AGB estimation.

A Three-Component Decomposition of the Change in Relative Poverty: An Application to China

China has eliminated absolute poverty and begun to tackle relative poverty, yet the change in relative poverty in China has been less studied. In this paper, we develop a three-component decomposition of the change in relative poverty and apply it to analyze the relative poverty in China. The change in relative poverty is decomposed into identification, growth, and redistribution components. We compare the three-component decomposition with other decomposition methods in the existing literature and show the advantages of the former. Our study, using the China Family Panel Studies (CFPS) 2010–2018 data, shows that relative poverty is rising. Examining the periods of 2012–2014, 2014–2016, and 2016–2018, we show that the three components differ in their contribution to relative poverty. The identification component rises with income growth and increases relative poverty by 8.42%, 12.19%, and 12.55%, respectively. The growth component reduces the incidence of relative poverty by 8.34%, 11.24%, and 12.18%, respectively. In comparison, the redistribution component increases the incidence of relative poverty by 2.88%, 1.12%, and 6.60%, respectively.

Orthogonal Scattering Model-Based Three-Component Decomposition of Polarimetric SAR Data

New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute to the refined interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly prominent problem lies ahead of model decomposition methods when there are similar scattering models for a decomposition scheme. It is difficult to separate the polarization power of similar scattering mechanisms reasonably and robustly. Therefore, in this paper, we first analyze the necessity of orthogonality between scattering models. Following this, we propose two mutually orthogonal rank-1 scattering models, which can degenerate to mostly current scattering models. The orthogonality and adaptability of scattering models are considered in the model derivation. Simulated PolSAR data, real ALOS-2/PALSAR-2, GF-3, and E-SAR data are selected to validate the proposed method. As shown by the results, first, the proposed method enhances the double-bounce scattering contribution in urban areas and maintains the volume-scattering contribution in vegetation areas because it separates the polarization power of different scattering mechanisms more reasonably. Second, the proposed method is powerful in the robust interpretation of different ground targets, resulting from the orthogonality of scattering models. These two characteristics of orthogonal scattering models are expected to play a positive role in large-scale applications, especially in land-cover classification.

Turbulent channel flow controlled by traveling-wave-like body force mimicking oscillating thin films

To improve energy efficiency, flow control techniques for skin-friction drag and heat transfer with regard to wall turbulence are essential. This study performs direct numerical simulation of turbulent channel flows. The traveling-wave-like body force is employed as the flow control technique to break the similarity between momentum and heat transfer. The traveling wave control mimics the self-excited thin film in the corresponding experimental study. When the wave traveled slowly along the downstream direction, the skin-friction drag, heat transfer, and analogy factor were found to increase. Moreover, these parameters increased with an increase in the reference height of the traveling wave (hw). Flow visualization shows turbulence enhancement owing to the increase in hw. Three-component decomposition elucidates the difference between the control effect on the Reynolds shear stress and the turbulent heat flux.

Thermal response and surface recession of a carbon-phenolic charring heatshield of spacecraft: Numerical simulation and validation

Aero-thermal heating experienced by spacecraft as they enter planetary atmosphere is exorbitant. The response of a sacrificial thermal shielding system of a spacecraft to extreme heating depends on the unsteady dynamics of thermo-physico-chemical interaction of shielding material with high enthalpy plasma. This paper addresses a transient analysis being performed to elucidate these interaction phenomena and to approximate the thermal response and the surface recession of an iso-q model made of low-density, high-porosity carbon/phenolic ablative material. The newly developed finite element material response code of this study solved time-dependent governing equations, including two energy conservation equations (for internal- and surface energy). A three-component chemical decomposition model approximated the material decomposition rate. Darcy's law predicted the direction of pyrolysis gas flow as well as the pressure gradient across the homogeneously porous solid material. A shaver re-meshing technique refined the ablation front posterior to the surface recession. Benchmark solutions were computed and compared against available solutions to interrogate the fidelity of the proposed code. For an identical surface energy boundary condition, the proposed code predicted a material temperature in excellent agreement with the thermal response data inferred from the material response code “Amaryllis”. Besides, the predicted char contours were similar to that of a high enthalpy air-plasma-exposed iso-q specimen. The outcomes of this study will reinforce the confidence of spacecraft engineers on the proposed code for the aero-thermal analysis of next generation sacrificial thermal shielding systems.

Direct numerical simulation of the backward-facing step turbulent flow controlled by traveling wave-like body force

Direct numerical simulations of the incompressible turbulent flow over the backward-facing step have been performed. To control the separation flow, the traveling wave-like body force control is applied to the top surface of the step. We choose four different cases to discuss the control effect on the reattachment length: stationary wave cases with the opposite phase and the downstream and upstream traveling wave cases. The reattachment length decreased in all the cases; however, the decrease patterns differed. The stationary wave affects the velocity of the flow above the step, which reduces the reattachment length. In traveling wave cases, the strong separation bubble is generated upstream and released periodically (i.e., oscillation). The phase-average analysis shows that the period of the traveling wave dominates the oscillation of the separation bubble. The process of separation bubble oscillation can be divided into two parts. The negative body force instantly induces the flow downward after flow separation, which generates new separation bubbles and pushes out the original one. The positive body force induces the flow upward, which increases the size and rotation of the separation bubble. Therefore, the release of the separation bubble is attributed to this switch. The three-component decomposition clarifies contributions of traveling waves and turbulence.

An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component decomposition and multi-scale thresholding segmentation. Firstly, the polarimetric characteristics of the port water are analyzed using modified three-component decomposition. Secondly, the volume scattering power and the power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are used to extract the port water. Water and land are first separated by a global thresholding segmentation of the volume scattering power, in which the sampling region used for the threshold calculation is automatically selected by a proposed homogeneity measure. The interference water regions in the ports are then separated from the water by segmenting the PRDV using the multi-scale thresholding segmentation method. The regions of interest (ROIs) of the ports are then extracted by determining the connected interference water regions with a large area. Finally, ports are recognized by examining the area ratio of strong scattering pixels to the land in the extracted ROIs. Seven single quad-polarization SAR images acquired by RADARSAT-2 covering the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America are used to test the proposed method. The experimental results show that all ports are correctly and quickly detected. The false alarm rates are zero, the intersection of union section (IoU) indexes between the detected port and the ground truth can reach 75%, and the average processing time can be less than 100 s.

Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data

The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14–18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°–46°) were superior to low incidence angles (21°–27°) due to the overall higher accuracies.

Retrieval of Lunar Surface Dielectric Constant Using Chandrayaan-2 Full-Polarimetric SAR Data

For more than four decades, it has been known that the dielectric constant of the lunar surface can be retrieved from the Fresnel reflection coefficients. However, theoretical models have met with limited success in validating laboratory test results from the Apollo missions to date. This paper is the first study to focus on the use of high-resolution full-polarimetric synthetic aperture radar datasets for the retrieval of the dielectric constant of the lunar surface from the Fresnel reflection coefficients. We initially show that it is possible to retrieve the lunar dielectric constant via the classical Freeman-Durden Decomposition (FDD). The performance of the FDD algorithm is found to be unacceptable over regions with surface slopes and craters, and for sub-surface soil samples. Accurate estimation is not possible by simply replacing the volume scattering model in the FDD with popular and widely used volume scattering models. Therefore, a model-based three-component decomposition (TCD) algorithm for a robust retrieval of the lunar dielectric constant is proposed. The proposed TCD algorithm implements an efficient branching condition combined with double unitary matrix rotations and provides exceptionally accurate dielectric constant estimation. The proposed TCD algorithm is validated by using L band full-polarimetric datasets acquired by the Chandrayaan-2 mission over Apollo 12, Apollo 15, and Apollo 17 landing sites. Comparisons are also made with other three-component decomposition algorithms. Excellent agreement between the estimated values by the proposed TCD algorithm and the reference values for the dielectric constant, available from the literature, has been observed.

Hybrid Three-Component Scattering Power Characterization From Polarimetric SAR Data Isolating Dominant Scattering Mechanisms

Rapid advancements have been made in model-based decomposition techniques for polarimetric Synthetic Aperture Radar (PolSAR) data. Improvements have been primarily driven by including additional scattering models to the three-component model-based method first introduced by Freeman and Durden. Nevertheless, the three-component method is still extensively used due to its simplicity and ease of interpretability. Recently, the paradigm of the decomposition strategy has been changed to non-model types with notable success. Thus utilizing this new approach, we propose a hybrid (i.e., combining non-model and model-based) three-component methodology in this work. The proposed method primarily involves three steps: (i) the generalized eigendecomposition technique is first used to determine the optimum volume scattering power, (ii) the residual rank-2 coherency matrix (i.e., volume scattering model deducted) is appropriately transformed using two unitary transformations to decorrelate the odd and even bounce scattering components, and (iii) compute the odd and even bounce scattering power contributions using the newly developed scattering-type parameter obtained from the rank-2 matrix. Each step carries relevant physical significance that is appropriately addressed in this work. The proposed methodology is first demonstrated using some specific coherency matrices from canonical targets and a few matrices extracted from different landcover types from full-polarimetric SAR images. We then apply the proposed method over diverse landcover types using two full-polarimetric SAR images. We compare the results with the state-of-the-art three-component model-based decomposition techniques to validate the effectiveness of the proposed method that deals with the existing challenges of model-based decomposition methods.

Dielectric Constant Estimation of Lunar Surface Using Mini-RF and Chandrayaan-2 SAR Data

A new dielectric constant estimation approach for the lunar surface using Mini-RF synthetic aperture radar (SAR) data and Chandrayaan-2 SAR data is presented in this work. Both the SAR systems are based on a hybrid-polarimetry (hybrid-pol) configuration that transmits a circularly polarized wave and coherently measures the backscattered wave by dual orthogonal linearly polarized channels. From the three-component hybrid-pol SAR decomposition technique, the ratio of the Fresnel reflection coefficients for horizontal and vertical polarization transmission can be estimated. This ratio, which is referred to as the co-polarization ratio, is used by the proposed methodology to find the real value of the dielectric constant. For performance validation, the proposed method is implemented on the Mini-RF hybrid-pol SAR data acquired over Apollo 17 landing sites. The values of the real part of dielectric constant are estimated for six different regions covering the collection sites of six Apollo 17 samples: 72 441, 73 241, 74 241, 75 081, 76 001, and 79 135. The results obtained using the proposed method are found to be in good agreement with the laboratory-measured results of the corresponding samples. Furthermore, the proposed methodology is also implemented on the Chandrayaan-2 hybrid-pol SAR data acquired over the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Biot crater</i> region. Various small areas possessing different possible surface characteristics, situated inside and outside the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Biot crater</i> , are being analyzed for validation.

Sensitivity Study of X-Band Multiparametric SAR Data From Cabbage Fields

To make polarimetric synthetic aperture radar (SAR) accessible to mainstream applications, the true composition ratio of scattering mechanisms within a radar backscatter must be accurately identified. To validate polarimetric SAR decomposition techniques, a novel multiparametric SAR observation combined with a theoretical model simulation has been applied to rice paddies having simple vegetation structure on smooth and flat surfaces, where the surface scattering was small. In this article, cabbage fields in Miura city in Japan were selected as outdoor-grown vegetables, so the effect of exposed ground surfaces must be taken into account. In combination with the multiparametric SAR observation, a dominant scattering mechanism is reliably isolated through the theoretical characterization of the data by a discrete scatterer model (DSM). Suppressing unnecessary effects by row structures of the crop field, the volume scattering from cabbage leaves is identified as a dominant scattering mechanism over most incidence angles. On the other hand, the surface scattering is dominant for copolarizations only at small incidence angles. The analysis also indicates the possibility that greater soil moisture could be kept by roots of mature cabbages. To demonstrate the validation of polarimetric decomposition by the results, a popular three-component decomposition was applied to the observation. Differences between the decomposition and the simulation results were analytically explained by features of the backscatter from cabbage fields. Model-based polarimetric decompositions shall be improved through this approach.

Rice Inundation Assessment Using Polarimetric UAVSAR Data.

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.

Framework for Reconstruction of Pseudo Quad Polarimetric Imagery from General Compact Polarimetry

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.