Polarization Orientation Angle Research Articles

A Geometry-Compensated Sensitivity Study of Polarimetric Bistatic Scattering for Rough Surface Observation

The use of bistatic polarimetric SAR for rough surface observation has attracted increasing interest in recent years, with its acquisition of additional polarimetric information. In this paper, we investigate the sensitivity of polarimetric variables to soil moisture and surface roughness, with the intention of locating favorable bistatic geometries for soil moisture retrieval. However, in the bistatic setting, the expanded imaging geometry is convolved with the polarimetric scattering response along with the in-scene variations in the soil moisture and surface roughness. The probing polarization states continuously evolve with the bistatic geometry, incurring varying polarization orientation angles. In this investigation, we propose to first compensate the bistatic polarimetric observations for the geometry-induced polarization rotation. Simulations based on a two-scale rough surface scattering model are then used to evaluate the optimal imaging geometry for the best sensitivity to the soil moisture content. We show the different sensing geometries associated with a full list of common polarimetric variables, as we seek favorable bistatic geometries in non-specular directions. The influences of both surface roughness scales are evaluated, with the small-scale roughness parameter imposing the greatest limitation on our results.

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

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.

Underwater polarization de-scattering imaging based on orthogonal polarization decomposition with low-pass filtering

Previous polarization imaging methods tend to assume that the degree of polarization of the target reflected light is a constant, while the targets with nonuniform polarization characteristics are less considered. The required background area or prior knowledge hinders the practical application of underwater polarization imaging technology. In this paper, we analyzed the basic physical model of underwater imaging, and proposed a new underwater polarization de-scattering method by considering orthogonal polarization decomposition of both the target reflected and backscattered light. The polarized-difference intensity of the backscattered light can be directly calculated from the polarization orientation angle of the two horizontal decomposition components. Meanwhile, a low-pass filter was used to suppress noise and globally evaluate the degree of polarization of the backscattered light. The proposed method can automatically distinguish the underwater targets polarization characteristics, and no background area or any prior information is required. The laboratory simulation experimental results demonstrate that this method can effectively improve the quality of underwater images under different conditions.

Resolving the subwavelength width of nanoslits by full-Stokes polarization analysis of scattered light.

Due to the diffraction limit, subwavelength nanoslits (whose width is strictly smaller than λ/2) are hard to resolve by optical microscopy. Here, we overcome the diffraction limit by measuring the full Stokes parameters of the scattered field of the subwavelength nanoslits with varying width under the illumination of a linearly-polarized laser with a 45° polarization orientation angle. Because of the depolarization effect arising from the different phase delay and amplitude transmittance for TM polarization (perpendicular to the long axis of slit) and TE polarization (parallel to the long axis of slit), the state of polarization (SOP) of the scattered light strongly depends on the slit width for subwavelength nanoslits. After correcting for residual background light, the nanoslit width measured by the SOP of scattered light is consistent with the scanning electron microscopy (SEM) measurement. The simulation and experiment in this work demonstrate a new far-field optical technique to determine the width of subwavelength nanoslits by studying the SOP of the scattered light.

Control of Polarization Orientation Angle of Scattered Light Based on Metasurfaces: −90° to +90° Linear Variation

Metasurfaces can be used to precisely control polarization state of the scattered light. Here, we present a metasurface-based terahertz device. On the one hand, it serves as a high performance linear polarization converter in transmission of over 80% power with weak reflection. It is capable of rotating linear polarization orientation angle with respect to x-axis continuously from −90° to +90° at 0.84 THz. On the other hand, it serves as a circular polarizer. It can transform a linear polarized wave into a circular polarized wave at 2.49 THz. The transmitted and reflected field are both circular polarized with 50% power. The proposed device with dual functionalities can be applied to modulate the polarization state of the signal in THz wireless communication.

A hybrid network for PolSAR image classification based on polarization orientation angles

ABSTRACT Polarimetric synthetic aperture radar (PolSAR) image classification is a significant task of PolSAR applications. In recent years, deep learning-based methods using spatial features have achieved satisfactory results in PolSAR classification. However, there are still some important features left to be exploited. One is the target scattering orientation diversity feature that contains rich hidden information. In this letter, a hybrid network is proposed to model the target scattering orientation diversity feature and spatial feature. First, the polarimetric scoherency matrix is expanded to polarization coherency matrix sequence by different polarization orientation angles (POAs). Then, the long short-term memory (LSTM) network and the convolutional neural networks (CNN) are proposed to process the polarization coherency matrix sequence and the square neighbourhood of pixels, respectively. Finally, the features of the two sub-networks are combined to improve the classification accuracy. The experiments demonstrate that the proposed method can obtain superior and robust performance.

A Comparison Study of Polarization Orientation Angle Estimation for Rough Terrain Surface

Estimation of polarization orientation angle (POA) is of general interest in polarimetric SAR (PolSAR) image processing because it is useful to remove the orientation-induced polarimetric distortion but also to infer physical target parameters. Under the assumption of scatter reflection symmetry, POA can be readily estimated from a circular polarization basis formulation or equivalently from zeroing the Pauli-basis correlation term <inline-formula><tex-math notation="LaTeX">${{\bm{T}}}_{23}$</tex-math></inline-formula>. This solution does not account for a possible presence of helical components; alternatively, we developed a general formulation for POA estimation using Touzi parameterization that explicitly contains a helical angle. When applied to PolSAR imagery of rough terrain surface, those two estimators generally give different results. Based on the POA derived from co-registered digital elevation model (DEM) data, we find the former estimator can underestimate the POA for rough terrain surface. In this paper, we further investigate the underlying cause. Although these two POA estimators differ in whether assuming zero helicity or not, the inherent helicity on the rough surface scattering is negligible; instead, the difference in the estimated POA is mainly attributed to the degree of mixing of a secondary scattering mechanism. This mixing impact can be significant even for rough terrain with only light vegetation cover, suggesting a practical need for inclusion of alternative estimators.

Composite Sequential Network With POA Attention for PolSAR Image Analysis

The scattering response of polarimetric synthetic aperture radar (PolSAR) data is strongly target orientation-dependent. Formulating the polarimetric matrix as sequential data by rotating the polarimetric matrix along the radar line of sight would provide rich information about land-cover properties. In this work, we propose a composite sequential network (CSN) with polarization orientation angle (POA) attention to model the polarimetric coherency matrix sequence and explore target scattering orientation diversity features. Three major factors strengthen the proposed method for PolSAR image analysis. First, CSN improves the feature comprehensiveness by extending the interpretation mode of PolSAR data from spatial polarization to spatial polarization orientation. In this way, CSN could describe polarimetric response dynamics at different orientations. Second, a two-stream composite network with both real- and complex-valued convolutional long short-term memory (ConvLSTM) network is proposed to process the diagonal and off-diagonal elements of the coherency matrix sequence, respectively. Compared to existing real-/complex-valued networks, the CSN explores the significant phase information of the off-diagonal elements by operations in the complex domain. Meanwhile, CSN prevents padding 0 meaninglessly in the imaginary part of the real-valued diagonal elements. Third, during the sequential modeling of the polarimetric matrix, a POA attention mechanism is proposed. Equipped with POA-sensitive decomposition loss, the CSN attends to substantial POA range derived by targets' physical scattering mechanism and learns features closely related to the scattering mechanism. Extensive experiments and analysis on land-cover classification demonstrate the proposed method's robustness and excellence.

A Facet Statistical Model for Polarimetric Passive Microwave Signatures of Mountainous Terrain

The impact of topography on satellite passive microwave data hinders the soil moisture retrievals over mountainous regions. Current two-scale facet statistical microwave emission models for ocean-like rough surfaces and mountainous terrain use a 2-D slope distribution which is not explicitly linked to the small-scale scattering. We have developed a modified two-scale polarimetric model in sensor coordinates that decomposes the total emission into two statistically independent components, the incidence angle tilt and polarization orientation, which are directly and uniquely linked to the small-scale scattering and can thus provide insight into the physical process of scattering from topographic roughness. Based on tilt angle and polarization orientation angle (POA) distributions derived from digital elevation model (DEM) data, we examined the azimuthal modulation of the polarimetric signatures and their dependence on the soil moisture and vegetation conditions. We also compared the simulated third Stokes parameters against WindSat satellite observations over four regions in the American Southwest. Good agreements are found between the model simulation and WindSat observations.

Three-Dimension Polarimetric Correlation Pattern Interpretation Tool and its Application

Polarimetric radar can acquire complete polarization information and is widely used in many applications. However, target orientation relative to the radar line of sight usually exhibits significant influences on the scattering mechanisms. Recently, such target scattering diversity has been successfully characterized and utilized with the polarimetric rotation domain interpretation techniques. In radar polarimetry, target scattering responses are affected by both polarization orientation angle and polarization ellipticity angle. In this vein, this work aims at exploring and utilizing the complete target scattering diversity by extending polarimetric rotation domain techniques to the polarization ellipticity angle dimension. The main idea is to develop a three-dimension polarimetric correlation pattern (3-D PCP) interpretation tool, which can visualize and exhibit targets’ polarimetric rotation domain properties in terms of both the polarimetric orientation and ellipticity angles. Then, a set of global, local, and mutual polarimetric features are proposed to characterize the responses of a 3-D PCP interpretation tool. Especially, the curvatures in differential geometry are first introduced to describe the properties of the 3-D surface. The performance of these new polarimetric features is investigated with spaceborne polarimetric synthetic aperture radar (PolSAR) data. Experimental results demonstrate the advantage of the proposed 3-D PCP features in enhancing the target clutter ratio (TCR), especially for the weak ship area. Based on this, a non-local superpixel-level contrast measure (NSLCM) method for ship detection is proposed. Pure sea samples can be determined adaptively for salient map construction. Comparison results demonstrate better detection performance for both the inshore dense ship area and weak ship area.

A Novel Technique for Inversion of Rotation Angles in Built-Up Areas

In previous publications, it is generally believed that the rotated buildings in built-up areas can induce polarization orientation angles, which cause overestimation of the volume scattering contribution. However, we found that the rotation effect of the double-bounce scattering wall-ground structures cannot be accurately expressed by an orientation angle. In this letter, dihedrals are used to simulate the wall-ground structures, and a novel technique for inversion of rotation angles in built-up areas is proposed. This letter is committed to directly inverting rotation angles rather than orientation angles. Then the rotation angles are compensated into the measured scattering matrix instead of deorienting the coherence matrix. C-band GF-3 and L-band ALOS-2 polarimetric data of San Francisco are used to verify the effectiveness of the proposed inversion technique.

Underdetermined Estimation of Multiple Parameters for Partially Polarized Signals With Dual-Polarized Coprime Array

This letter considers the problem of underdetermined estimation of direction-of-arrival (DOA) and polarization parameters, including the degree of polarization (DOP), polarization orientation angle (POA), and polarization ellipticity angle (PEA), for partially polarized (PP) signals with a dual-polarized coprime array. Firstly, the increased dimension due to dual-polarized sensors is reduced by adding two covariance matrices, which facilitates the coarray construction. Then, the holes in the coarray of coprime array are interpolated via utilizing the annihilating relation. At last, the DOAs and array noise variance are obtained by solving an annihilating relation based optimization problem and then used to reconstruct the coherency matrices via the weight least squares (WLS). The polarization parameters can be estimated by performing eigendecomposition on the reconstructed coherency matrices. We show the underdetermined estimation of array noise variance which is difficult for conventional methods is vital for accurate underdetermined estimation of the polarization parameters. The performances of the proposed method are verified by numerical simulations and compared with other representative methods including the Cramer-Rao bound (CRB).

Polarimetric Model-Based Decomposition with Refined Double-Bounce Orientation Angle and Scattering Model

Oriented manmade targets can produce significant cross-polarization power. The scattering mechanism interpretation of them is still challenging. Within the framework of traditional scattering models, the scattering mechanism of oriented manmade targets will be interpreted as volume scattering. Recently, many advanced approaches have been proposed to mitigate the cross-polarization terms of the coherency matrix or distribute the power of cross-polarization to new scattering models, such as orientation angle compensation and multiple scattering components decomposition. Among these methods, the general model-based decomposition with physically meaningful double-bounce and odd-bounce scattering models has been proposed by modeling their independent orientation angles and becomes a widely accepted method. However, the two vital parameters of generalized scattering models: double- and odd-bounce orientation angles are derived through nonlinear optimization procedure. These generalized models lead to a heavy computation burden for parameters inversion. In this paper, we disclose the latent relationship between the double-bounce orientation angle and polarization orientation angle by data fitting experiments. With this simplified relationship, a refined double-bounce scattering model is established. Then, the odd-bounce orientation angle can be derived through equations. In this way, the nonlinear optimization procedure can be converted to a linear solution. A fast generalized model-based decomposition is developed thereafter. The main contribution of this work is to inherit the generalized models while speeding up the parameter calculation procedure. The comparison studies are carried out with X-band airborne PiSAR, L-band spaceborne ALOS-2, and C-band spaceborne Radarsat-2 PolSAR datasets. Compared with the state-of-the-art approaches, the proposed decomposition achieves improved interpretation performance from both visual and quantitative investigations especially for oriented built-up areas.

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

AbstractPolarimetric 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.

Influencing factors analysis of supercontinuum laser monochromator in calibration of polarization remote sensors

Relative spectral responsivity and the in-band spectral response non-consistency of polarization remote sensors are the basic parameters for laboratory calibration. In this paper, a spectrally-resolved calibration facility was assembled for the calibration of relative spectral responsivity by using novel supercontinuum laser and monochromator. Taking three polarization channels of 490 nm, 670 nm and 910 nm as examples, the relative spectral responsivity of the channel polarization remote sensors was obtained under the two states of the monochromator output light directly and the monochromatic light steered into an integrating sphere respectively, and the polarization state of the calibrated light source was measured under the two states. The experimental results show that there are obvious changes in degree of linear polarization (DOLP) and polarization orientation angle in the mode of direct output light of monochromator, and the non-consistency of in-band spectral response of the three polarization channels were 0.91%, 4.25% and 1.06%, respectively. When the monochromatic light steered into the integrating sphere, the DOLP of the light source decreases significantly, and the non-consistency of in-band spectral response were 0.15%, 0.47% and 0.57%, respectively, which indicates that depolarized light source can be effectively applied to the spectral responsivity calibration of polarization remote sensors. And the system-level calibration facility based on supercontinuum laser and monochromator has wide application prospects in the calibration of polarization remote sensors.

Minimizing scattering-induced phase errors in differential interference contrast microscopy.

.Significance: Differential interference contrast (DIC) microscopes allow noninvasive in vivo observation of transparent microstructures in tissue without the use of fluorescent dyes or genetic modification. We show how to modify a DIC microscope to measure the sample phase distribution accurately and in real-time even deep inside sample tissue.Aim: Our aim is to improve the DIC microscope’s phase measurement to remove the phase bias that occurs in the presence of strong scattering.Approach: A quarter-wave plate was added in front of the polarization camera, allowing a modified phase calculation to incorporate all four polarization orientation angles (0 deg, 45 deg, 90 deg, and 135 deg) captured simultaneously by the polarization camera, followed by deconvolution.Results: We confirm that the proposed method reduces phase measurement error in the presence of scattering and demonstrate the method using in vivo imaging of a beating heart inside a medaka egg and the whole-body blood circulation in a young medaka fish.Conclusions: Modifying a polarization-camera DIC microscope with a quarter-wave plate allows users to image deep inside samples without phase bias due to scattering effects.

Bio inspired synthesis of silver nanoparticles and its applications to spin – orbit interactions of light

Spin–orbit interaction of light serves as an important property of light, which deals with the study of polarization and phase modulations in the light beam. These studies are essential and principal characeristics of light beam that have been used for most of the nanophotonics applications. Silver nanoparticles (Ag NPs) prepared via biosynthesis are used for one of such nanophotonics application in scattering via studying the light scattered through these nanoparticles. The silver nanoparticles Ag NPs were synthesized using green method, where reduction of silver ions to silver nanoparticles happen during the reaction of aqueous solution of Ag NO3 with the biomolecules present in fresh leaf extract of Coleus amboinicus plant. The nanoparticles were characterized using UV-visible (UV–vis) spectroscopy, Transmission electron microscopy (TEM) and Fourier Transform Infrared (FTIR) spectroscopy. TEM analysis shows the wide size distribution of spherical shape nanoparticles with 80 nm average size. The study of polarization and phase changes in the scattered light field has been carried out using Stokes polarimetry in forward direction scattering. Under the preliminary measurements of Polarimetry, the modification in the polarization components was studied by demonstrating changes in the Stokes S2, S3 parameters, polarization orientation (ψ) and ellipticity angle (χ) using transverse magnetic (TM) polarized Gaussian light beam.

Investigation on the Effects of Polarization Orientation Angle Shift in the UAVSAR Polarimetric Decomposition

The polarization orientation angle (POA) shift results in higher cross-polarization intensity in the HV and VH channels making the reflection asymmetrical. The L-band quad-pol dataset acquired over the Fujiyama mountain, Japan by the Uninhabited Aerial Vehicle Synthetic Aperture Radar of JPL/NASA was used in this study to analyse the effect of POA shift in the polarimetric decomposition techniques. The dataset was first compensated for the POA shift using the circular polarization algorithm, and a mean polarization orientation angle shift of − 4.23° was estimated. The Yamaguchi 3-component model-based decomposition and the H/A/Alpha eigenvector-based decomposition techniques were performed on the dataset before and after POA compensation. It was found that there was an overestimation of volume scattering component and underestimation of double-bounce scattering component before POA compensation in the Yamaguchi 3-component decomposition technique. After the POA compensation, there was a significant reduction in the volume scattering component observed at the non-vegetated areas and increase in double-bounce scattering in the same areas. There was no considerable change observed in the surface scattering component because of its roll invariant nature. Since the H/A/Alpha decomposition parameters were also independent of the orientation angle change because of their roll invariance property, there was no change observed in those parameters after POA compensation.

A Novel Method for Polarization Orientation Angle Estimation Over Steep Terrain and Comparison of Deorientation Algorithms

This article proposes a novel polarization orientation angle (POA) estimation algorithm for steep terrain by applying physical scattering characteristics of natural surface. The algorithm extends the range of POA from [-45°, 45°] to (-90°,90°] and achieves consistent estimation with the widely used circular polarization algorithm (CPA) and minimum cross-polarization algorithm (MXPA) over flat area, but avoids the POA wrapping caused by the restriction of CPA and MXPA over precipitous area. Besides, detailed comparison among these deorientation algorithms is presented for clarifying their relations and analyzing the wrapping and unwrapping issue of POA. Both simulated data of Bragg scattering model and PolSAR data of L-band ALOS PALSAR and P-band NASA/JPL AIRSAR are used to substantiate the proposals.