Polarization Decomposition Research Articles

Weak Target Detection Based on Full-Polarization Scattering Features under Sea Clutter Background

Aiming at the low observable target detection under sea clutter backgrounds, this paper emphasizes the exploration of distinguishable full-polarization features between target and sea clutter echoes. To overcome the shortcomings of the existing polarization feature-based methods, the full-polarization features of sea clutter are modeled and analyzed in detail by using Van Zyl polarization decomposition. Then, three polarimetric features (the relative surface scattering energy, the relative dihedral scattering energy and the relative diffuse scattering energy) are extracted from the fully polarimetric radar sea clutter echoes, which improve the feature differences between sea clutter and targets. And a tri-polarimetric feature detector with constant false alarm rate (CFAR) is constructed based on the fast convex hull learning algorithm. The experimental results on the real measured IPIX radar datasets prove that the proposed full-polarization feature detector obtains more competitive detection performance and lower computational complexity than the several existing feature-based detectors.

Numerical Modeling of a Low-Cobalt All-Solid-State Cell with Ceramic Electrolyte Using a Deformable Geometry

All-solid-state batteries with a lithium negative electrode and a ceramic electrolyte are key toward high energy density. To ensure a safe, fast, accurate, and cost-effective development of this technology, the experimental methodology must be supported by the numerical modeling approach. This work proposes and describes an electrochemical model of a Li7La3Zr2O12 (LLZO) and Ni-rich NMC-based lithium cell with a deformable lithium negative electrode. Simulations were computed using the finite element method at different operating conditions to demonstrate the scope of the modeling work. Discharge rate tests, deformation tracking, geometric defect investigation, and polarization decomposition are described. Theoretical validation of the mass balance, the stripping rate, the ohmic polarization, and the mesh deformation demonstrated the consistency of the volumetric deformation strategy. We demonstrated in this study a deformable modeling strategy, which was found to be useful for the electrostripping analysis of anodic geometry defects during discharge. Non-uniformity in the lithium stripping rate was found along the anodic interface with defects, and this non-uniformity was accentuated with a higher discharge rate. The cell’s discharge potential was decomposed by considering the equilibrium potential and the polarizations of the main components of the cell. This post-processing was found to be useful for the understanding of the cell’s behavior.

Canopy active fuel loads estimation with C-band PolSAR by combining polarization decomposition and vegetation scattering model: a case study in Southwestern China

ABSTRACT The forest canopy active fuel load (CAFL) is easily flammable that is usually consumed in the flaming front, and is a critical factor in crown fire early prediction, suppression and response. Previous studies universally relied on optical remote sensing data, whose effectiveness is extremely limited in regions with cloud cover and haze, such as the southwest of Sichuan, China. Synthetic aperture radar (SAR) can work 24 h and is not affected by weather conditions. This study aims to evaluate the potential of volume backscattering coefficients extracted through decomposition of polarimetric SAR (PolSAR, Radarsat-2 C band) for CAFL estimation. The polarimetric decomposition method used in this study was based on Freeman-Durden decomposition. The separated volume backscattering coefficients were used to estimate CAFL based on the variant of the extended water cloud model (VEWCM). For comparison, the SAR raw observations and extended water cloud model (EWCM) were also used to estimate CAFL. The performance of these two models was validated through quantitative assessment, utilizing CAFL measurements obtained from southwest Sichuan and the leave-one-out cross-validation method. Results show that the VEWCM (R2 = 0.69, RMSE = 0.87 tons/ha) performed better than EWCM (R2 = 0.60, RMSE = 1.48 tons/ha) especially in HV polarization. We concluded that PolSAR has the potential to improve CAFL estimation and further assist with pre-fire risk warnings, fire suppression responses, and post-fire effects assessment.

Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning

Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which can play a crucial role in the early detection of abnormal tissue morphology. We propose a transmission-based Stokes-Mueller microscope for quantitative analysis of the microstructural properties of the tissue specimen. The Stokes-Mueller based polarization microscopy provides significant structural information of tissue through various polarization parameters such as degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), anisotropy (r) and Mueller decomposition parameters such as diattenuation, retardance and depolarization. Further, by applying a suitable image processing technique such as Machine learning (ML) output images were analysed effectively. The support vector machine image classification model achieved 95.78% validation accuracy and 94.81% testing accuracy with polarization parameter dataset. The study's findings demonstrate the potential of Stokes-Mueller polarimetry in tissue characterization and diagnosis, providing a valuable tool for biomedical applications.

Assessment of Sea-Ice Classification Capabilities during Melting Period Using Airborne Multi-Frequency PolSAR Data

Sea-ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, primarily due to meltwater complicating the distinguishability of sea-ice types. In response to this issue, this study introduces a novel method for classifying sea ice during the Bohai Sea’s melting period. The method categorizes sea ice into five types: open water (OW), gray ice (Gi), melting gray ice (GiW), gray–white Ice (Gw), and melting gray–white Ice (GwW). To achieve this classification, 51 polarimetric features are extracted from L-, S-, and C-band PolSAR data using various polarization decomposition methods. This study assesses the separability of these features among different combinations of sea-ice type by calculating the Euclidean distance (ED). The Support Vector Machine (SVM) classifier, when employed with single-frequency polarimetric feature sets, achieves the highest accuracy for OW and Gi in the C-band, GiW in the S-band, and Gw and GwW in the L-band. Remarkably, the C-band features exhibit the overall highest accuracy when compared to the L-band and S-band. Furthermore, employing a multi-dimensional polarimetric feature set significantly improves classification accuracy to 94.55%, representing a substantial enhancement of 9% to 22% compared to single-frequency classification. Benefiting from the performance advantages of Random Forest (RF) classifiers in handling large datasets, RF classifiers achieve the highest classification accuracy of 95.84%. The optimal multi-dimensional feature composition includes the following: L-band: SE, SEI, α¯, Span; S-band: SEI, SE, Span, PV-Freeman, λ1, λ2; C-band: SE, SEI, Span, λ3, PV-Freeman. The results of this study provide a reliable new method for future sea-ice monitoring during the melting season.

Multifunctional Polarization Converters Based on Linear-to-Circular Polarization Decomposition Reflective Surfaces

Multifunctional Polarization Converters Based on Linear-to-Circular Polarization Decomposition Reflective Surfaces

Single-Season Rice Area Mapping by Combining Multi-Temporal Polarization Decomposition Components and the Two-Stage Segmentation Method

Recently, Synthetic Aperture Radar (SAR) data, especially Sentinel-1 data, have been increasingly used in rice mapping research. However, current studies usually use long time series data as the data source to represent the differences between rice and other ground objects, especially other crops, which results in complex models and large computational complexity during classification. To address this problem, a novel method for single season rice mapping is proposed, based on the principle that the scattering mechanism of rice paddies in the early flooding period is strongly influenced by water bodies, causing the volume scattering to be lower than that of other crops. Thus, a feature combination that can effectively and stably extract rice planting areas was constructed by combining multi-temporal volume scattering in the early flooding period of rice using dual-polarization SAR data, so that a simple semantic segmentation model could realize high-precision rice mapping tasks. A two-stage segmentation structure was introduced to further improve the mapping result with the Omni-dimensional Dynamic Convolution Residual Segmentation model (ODCRS model) as the bone model. In the experiment, Suihua City, Heilongjiang Province was selected as the study site, and the VH/VV polarized data of Sentinel-1 satellite in 2022 was used as the data source. The mapping accuracy of the ODCRS model was 88.70%, and the user accuracy was 84.19% on the field survey data. Furthermore, experiments with different years and regions also proved the effectiveness and stability of the proposed method.

Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.

Optimizing Catalyst Layer Structure with Carbon Nanofiber Additive for Better Performance of Proton Exchange Membrane Fuel Cell

Increasing the power density of proton exchange membrane fuel cell (PEMFC) is essential for its commercialization. Cathode catalyst layer (CCL) carbon additive is an effective method that can improve membrane electrode assembly (MEA) performance under high current density. In this work, carbon nanofiber (CNF) is used as CCL additive and its mechanism is studied. The influence of CNF on the CCL structure is studied by scanning electron microscope (SEM), nitrogen adsorption method and contact angle experiment. Meanwhile, the mechanism is examined by using polarization decomposition and impedance analysis combined with the result in characterization experiments. The results show that: (1) the MEA used in this work has the best performance when adding 1 wt%. The voltage rises 74 mV (15.42%) at 2 A cm−2; (2) CNF varies the MEA performance mainly through changing the hydrophobicity of CCL, influencing the mass transport resistance and the concentration polarization. The mass transport resistance decreases 57.18% at 2 A cm−2; (3) activation polarization values have a negative linear correlation with the peak volume values of the primary pores at around 4 nm in diameter. This study provided a detailed understanding of the mechanism of CNF additive in CCL and will be helpful for improving PEMFC performance.

Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data

Abstract Distinguishing different kinds of igneous rocks is difficult because of their subtle differences. Synthetic aperture radar (SAR) is sensitive to rock surface morphology, which can help a lot in classification. Dual-pol SAR data have the advantages of low cost, but few articles are using only dual-pol SAR data for igneous rock classification. In this study, we explored the performance of dual-pol SAR data in distinguishing granitoid, tuff, and syenite porphyry. Backscatter coefficients, polarization decomposition parameters, and texture features from gray-level co-occurrence matrix extracted by Sentinel-1 or PALSAR were classified using several machine learning algorithms. The results are as follows. First, the texture information has greater potential for igneous rock classification, but the polarization decomposition parameters contribute less. Second, after comparing machine learning algorithms, AdaBoost algorithm has the highest overall accuracy for either C-band or L-band SAR data. C-band SAR data provide better classification results than L-band. Finally, tuff is the easiest igneous rock to be successfully classified, and L-band dual-pol SAR data have advantages in the discrimination of syenite porphyry. This study outlines the effectiveness of dual-pol SAR data for igneous rock classification, which will help to select SAR data of appropriate wavelengths for specific types of lithology discrimination.

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.

Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network

Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method.

Vegetation descriptors from Sentinel-1 SAR data for crop growth monitoring

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q, which is used to generate three vegetation descriptors: the co-pol purity parameter (mcp), the pseudo-scattering angle (θcp), and the pseudo-scattering entropy (Hcp). Further, a novel unsupervised clustering framework, founded on Hcp and θcp, has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that mcp,θcp, and Hcp exhibit regular changes at different growth stages of crops from planting to maturity, with mcp and θcp gradually decreasing while Hcp gradually increasing. Within the entire phenology window, θcp changes by approximately 42°, while both mcp and θcp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources.

Crop Type Mapping Based on Polarization Information of Time Series Sentinel-1 Images Using Patch-Based Neural Network

Large-scale crop mapping is of fundamental importance to tackle food security problems. SAR remote sensing has lately received great attention for crop type mapping due to its stability in the revisit cycle and is not hindered by cloud cover. However, most SAR image-classification studies focused on the application of backscattering characteristics with machine learning models, while few investigated the potential of the polarization decomposition and deep-learning models. This study investigated whether the radar polarization information mined by polarization decomposition, the patch strategy and the approaches for combining recurrent and convolutional neural networks (Conv2d + LSTM and ConvLSTM2d) could effectively improve the accuracy of crop type mapping. Sentinel-1 SLC and GRD products in 2020 were collected as data sources to extract VH, VV, VH/VV, VV + VH, Entropy, Anisotropy, and Alpha 7-dimensional features for classification. The results showed that the three-dimensional Convolutional Neural Network (Conv3d) was the best classifier with an accuracy and kappa up to 88.9% and 0.875, respectively, and the ConvLSTM2d and Conv2d + LSTM achieved the second and third position. Compared to backscatter coefficients, the polarization decomposition features could provide additional phase information for classification in the time dimension. The optimal patch size was 17, and the patch-based Conv3d outperformed the pixel-based Conv1d by 11.3% in accuracy and 0.128 in kappa. This study demonstrated the value of applying polarization decomposition features to deep-learning models and provided a strong technical support to efficient large-scale crop mapping.

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

Accurate land cover classification (LCC) is essential for studying global change. Synthetic aperture radar (SAR) has been used for LCC due to its advantage of weather independence. In particular, the dual-polarization (dual-pol) SAR data have a wider coverage and are easier to obtain, which provides an unprecedented opportunity for LCC. However, the dual-pol SAR data have a weak discrimination ability due to limited polarization information. Moreover, the complex imaging mechanism leads to the speckle noise of SAR images, which also decreases the accuracy of SAR LCC. To address the above issues, an improved dual-pol radar vegetation index based on multiple components (DpRVIm) and a new LCC method are proposed for dual-pol SAR data. Firstly, in the DpRVIm, the scattering information of polarization and terrain factors were considered to improve the separability of ground objects for dual-pol data. Then, the Jeffries-Matusita (J-M) distance and one-dimensional convolutional neural network (1DCNN) algorithm were used to analyze the effect of difference dual-pol radar vegetation indexes on LCC. Finally, in order to reduce the influence of the speckle noise, a two-stage LCC method, the 1DCNN-MRF, based on the 1DCNN and Markov random field (MRF) was designed considering the spatial information of ground objects. In this study, the HH-HV model data of the Gaofen-3 satellite in the Dongting Lake area were used, and the results showed that: (1) Through the combination of the backscatter coefficient and dual-pol radar vegetation indexes based on the polarization decomposition technique, the accuracy of LCC can be improved compared with the single backscatter coefficient. (2) The DpRVIm was more conducive to improving the accuracy of LCC than the classic dual-pol radar vegetation index (DpRVI) and radar vegetation index (RVI), especially for farmland and forest. (3) Compared with the classic machine learning methods K-nearest neighbor (KNN), random forest (RF), and the 1DCNN, the designed 1DCNN-MRF achieved the highest accuracy, with an overall accuracy (OA) score of 81.76% and a Kappa coefficient (Kappa) score of 0.74. This study indicated the application potential of the polarization decomposition technique and DEM in enhancing the separability of different land cover types in SAR LCC. Furthermore, it demonstrated that the combination of deep learning networks and MRF is suitable to suppress the influence of speckle noise.

Composition, structure and electrochemical performance of LiSiPSCl electrolyte with Li/Li-In anodes in all-solid-state batteries

The lithium silicon phosphor sulfo-chloride (LiSiPSCl) with a Li10GeP2S12-type structure is one of the most promising solid electrolytes (SEs) for all-solid-state lithium-ion batteries (ASSLIBs) due to its currently highest ionic conductivity at RT. In this work, three kinds of LiSiPSCl electrolytes, i.e. Li7.59(10)Si1.95(9)P1.65(7)S11.64(1)Cl0.36(1) (SE-450), Li7.67(12)Si1.90(1)P1.67(1)S11.61(2)Cl0.39(2) (SE-475) and Li7.58(5)Si1.75(4)P1.79(2)S11.54(4)Cl0.46(4) (SE-500), were prepared at 450 / 475 / 500 °C for 8 h through a solid-state method. The assembling of blocking / symmetrical / semi-blocking cells and ASSLIBs, and electrochemical measurements were carried out by coupling the three SEs with two kinds of anodes (Li metal and Li-In alloy) and one cathode of LiNbO3-coated LiNi0.8Co0.1Mn0.1O2 (NCM@LNO). Studies were focused on the relationship among chemical compositions, phase structures and electrochemical properties, as well as the electrolyte/electrode interfacial stability. Results show that LiSiPSCl electrolytes had favorable interfacial stability with Li-In anode and exhibited low polarization voltage (< 20 mV) and decomposition current (< 0.01 mA). The optimized NCM@LNO//SE-475//Li-In of ASSLIBs exhibited a stable long-term cycle performance with a capacity retention of 97.0% (128.4 mAh g−1) after 50 cycles and 81.1% (107.4 mAh g−1) after 100 cycles at 0.1 C. In addition, it exhibited a good rate performance while its capacity retention remained 99.3% (140.6 mAh g−1) after 39 cycles at 0.05 / 0.1 / 0.2 / 0.5 / 0.05 C.

Detection and recognition of dim and small targets in sea clutter background based on polarization decomposition

The consideration of the polarization scattering characteristics of radar echo in the detection of small and dim targets in a sea clutter background leads to high detection probability and high recognition accuracy. In this study, a target detection method based on canonical polar decomposition (CPD) was developed. In particular, a polarization matrix T was constructed for the radar echo of each range bin; the absolute value A and phase [Formula: see text] of T were extracted; the diagonal elements of the characteristic matrix H were extracted and converted into the singular spectrum form; and the negative entropies [Formula: see text] and [Formula: see text] of the singular spectrum were calculated. We set [Formula: see text]; then, the negative entropy value [Formula: see text] of each range bin was obtained, and the range bin of the minimum value was identified, which was the target position. Thereafter, a seven-component scattering power decomposition (7SD) was performed on T. The basic scattering structure of the target was analyzed, and a polar feature description word (PFDW) was formed to recognize the target characteristics. The effectiveness of the proposed method was verified by using the sea clutter dataset obtained with the IPIX radar. The comparison with the statistical model-based method is also made for indicating the advantage of the proposed method.

Linear-to-Dual-Circular Polarization Decomposition Metasurface Based on Rotated Trimming-Stub-Loaded Circular Patch

This paper presents a linear-to-dual-circular polarization metasurface decomposer, which decomposes a linearly polarized (LP) planar incident wave into a pair of circular polarized (CP) waves, namely, a right-handed circular polarized (RHCP) wave and a left-handed circular polarized (LHCP) wave, and scatters them into different directions. The proposed metasurface polarization decomposer is composed of a series of rotated trimming stub loaded circular patches. The two CP components are excited due to the perturbation introduced by the trimming stubs, and the different phase gradients added to the RHCP and LHCP components are realized by rotating the circular patches with different angles. A 12×12 metasurface polarization decomposer is designed, fabricated, and measured, which scatters the RHCP and LHCP into −30∘ and 30∘, respectively. The simulated and measured results agree well with each other, which demonstrates the proposed design.

Integrated metamaterial with the functionalities of an absorption and polarization converter.

A switchable and tunable dual-function absorber/polarization converter is presented in this work. The constitution of the structure, which incorporates patterned graphene and photosensitive silicon (Si), can minimize undesired optical losses. Simulated results show that when the Si is metallic, the structure behaves as a broadband absorption of more than 90% in the range of 1.45-3.36THz. Its peak absorption can be tuned from 22% to 99.8% by changing the Fermi energy of graphene. Furthermore, the interference theory analyzes the physical mechanism for broadband absorption. When the Si is in the dielectric state, the structure has a transmission polarization conversion function, which realizes the conversion from linear to cross-polarized waves. The polarization conversion ratio (PCR) is greater than 90% in the 3.82-4.43THz range. Meanwhile, the cross-polarization transmission can be dynamically tuned from 28% to 97%, and the PCR can also be tuned from 17% to 99.9% by adjusting the conductivity of the Si. The reason for realizing polarization conversion is explained by the polarization decomposition method. This study provides a design opinion of high-performance multifunctional tunable terahertz devices.

Mie Scattering Nanointerferometry for the Reconstruction of Tightly Focused Vector Fields by Polarization Decomposition

Tightly focused vector fields, which can be generated by focusing a light beam through a high-numerical-aperture objective, play an important role in nano-optics research. How to fully characterize this kind of field in the subwavelength scale is a challenging but important task. The Mie scattering nanointerferometry technique has been proposed to reconstruct the tightly focused vector field accurately. In this work, we theoretically demonstrate that the technique can be realized by collecting the transmitted light with two orthogonal polarization states simultaneously. Therefore, when nanoparticles are employed to scan the fields to be measured, more information of the scattering field can be acquired in the far field. This is helpful for solving the linear inverse scattering problem by reducing the number of scanning points, thus making the measurement more efficient.