Polarimetric Target Research Articles

A Two-Component Polarimetric Target Decomposition Algorithm with Grassland Application

A Two-Component Polarimetric Target Decomposition Algorithm with Grassland Application

Marine oil spill detection using improved polarimetric feature based on polarization SAR image

ABSTRACT Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications.

Deep learning-driven underwater polarimetric target detection based on the dispersion of polarization characteristics

Underwater target detection especially in turbidity environments is a crucial and challenging area of study due to its extensive applications. In this work, we propose an underwater target detection method that combines the dispersion of polarization characteristics with a neural network framework. The polarization dispersion distribution features are effective at reducing the influence of the scattering effect of particulate matter in a turbidity environment, and highlighting the polarization state variations, especially in the edge contour features between the target and background. According to the physical detection model of dispersion of polarization characteristics, the neural network is constructed to extract dispersion values of the angle of polarization (AOP) and degree of linear polarization (DOLP) from the underwater polarization image datasets with different turbidity levels, which we then used to train the network to map physical characteristic parameters of the target for the detection. When compared to target detection methods based on light intensity images and polarization characteristic images (AOP and DOLP), the experimental results indicate that the proposed network model with polarization dispersion images has a significant improvement in locating and identifying targets of different materials, particularly in high turbidity underwater environments. These results provide deeper understanding of polarization polarimetric target detection, and further improve the functionality of a polarimetric target detection optical system.

Quad-Pol SAR Data Reconstruction from Dual-Pol SAR Mode Based on a Multiscale Feature Aggregation Network

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing applications due to its ability to obtain full-polarization information. Compared to the quad-pol SAR, the dual-pol SAR mode has a wider observation swath and is more common in most SAR systems. The goal of reconstructing quad-pol SAR data from the dual-pol SAR mode is to learn the contextual information of dual-pol SAR images and the relationships among polarimetric channels. This work is dedicated to addressing this issue, and a multiscale feature aggregation network has been established to achieve the reconstruction task. Firstly, multiscale spatial and polarimetric features are extracted from the dual-pol SAR images using the pretrained VGG16 network. Then, a group-attention module (GAM) is designed to progressively fuse the multiscale features extracted by different layers. The fused feature maps are interpolated and aggregated with dual-pol SAR images to form a compact feature representation, which integrates the high- and low-level information of the network. Finally, a three-layer convolutional neural network (CNN) with a 1 × 1 convolutional kernel is employed to establish the mapping relationship between the feature representation and polarimetric covariance matrices. To evaluate the quad-pol SAR data reconstruction performance, both polarimetric target decomposition and terrain classification are adopted. Experimental studies are conducted on the ALOS/PALSAR and UAVSAR datasets. The qualitative and quantitative experimental results demonstrate the superiority of the proposed method. The reconstructed quad-pol SAR data can better sense buildings’ double-bounce scattering changes before and after a disaster. Furthermore, the reconstructed quad-pol SAR data of the proposed method achieve a 97.08% classification accuracy, which is 1.25% higher than that of dual-pol SAR data.

Polarization-space-time domain adaptive detection for the heterogeneous array

In this paper, to investigate the effect of different antenna attitudes on the problem of polarimetric target detection, we take the heterogeneous array (HA), which is a uniform linear array (ULA) composed of antennas with different attitudes, as the research object, and use the generalized likelihood ratio test (GLRT) criterion to derive an adaptive detection algorithm. Meanwhile, the probability of detection, as well as the probability of false alarm of this algorithm is presented. Through numerical simulations, we analyze the detection ability of the proposed algorithm in various scenarios, and the detection performance of the HA is compared with the ULA and the uniform linear array consisting of the orthogonal pairs of dipoles (ULAP). The simulation results indicate that the HA can improve its detection performance by exploiting the information in the polarization domain, and therefore, the polarimetric target detection ability of the array, which is composed of antennas with different attitudes, is proved.

Improved Threat Detection in Walk-Through Security Scanning Using an Optimized Polarimetric Target Decomposition Method

Reliable and convenient walk-through security scanning, which doesn't separate people or impede their movement, is an extremely challenging task. In this paper, a novel approach for a security check with an overhead observation and a polarimetric target decomposition is presented. The viewing angle of the scanner equals a side-looking airborne radar. However, it will be shown that the established polarimetric target decomposition methods of remote-sensing are not well suited for close-range radar imaging and need to be adapted due to the differences in the geometry of the imaging scenario. The usage of a multiple input and multiple output (MIMO) array and the shorter distance between array and target need a changed decomposition technique in order to distinguish between persons with or without worn threat objects. The differences between radar remote-sensing and close-range imaging scenarios are investigated. An optimized version of the Sato four-component polarimetric scattering decomposition is derived. The proposed close-range adjustment is applied to the model and investigated experimentally with a 4-to-12 GHz fully polarimetric MIMO imaging system. Polarimetric decomposition is carried out on defined test structures with known scattering mechanisms, as well as on mannequins and persons with different threat objects like guns or explosives. In test campaigns, promising results were achieved for a correct target decomposition in radar-based walk-through security scanning.

Model-Based Polarimetric Target Decomposition With Power Redistribution for Urban Areas

Model-Based Polarimetric Target Decomposition With Power Redistribution for Urban Areas

A Hybrid Polarimetric Target Decomposition Algorithm with Adaptive Volume Scattering Model

Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering model of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset over San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of pixels with negative powers, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues.

PDFL: Polarimetric Decomposition Feature Learning via Deep Autoencoder

Model-based polarimetric target decomposition (TD) generally solves scattering components and parameters under pre-set decomposition base, then decomposition features are also obtained. However, pre-set base could not be adjusted according to different scenes. Furthermore, solving the polarimetric parameters needs to explore additional information or consider limiting conditions to build equations, which is hard and easily to bring negative effects into decomposition features. To this end, we regard the TD as a process of learning decomposition base and features by deep learning. Then, the polarimetric decomposition feature learning (PDFL) model is proposed in this paper. Strictly, this model is not an incoherent TD method but a learning-based method. It dose not need to construct the parameter solution equations or fixed base. Then, the decomposition base and feature can be adaptively learned according to scattering characteristics of current dataset. Due to the characteristics of unsupervised reconstruction, deep auto encoder (DAE) is used as the model foundation. Then, some adjustments and constraints are utilized to make the DAE fit closely with TD. The encoder extracts latent vector from PolSAR data, then the decoder reconstructs pseudo data on this latent vector. The reconstruction can be regarded as the inverse process of TD, so the base matrix of decoder and the latent vector indicate the learned decomposition base and features when the model converges. The effectiveness of PDFL is verified on simulated and real PolSAR datasets. Compared with representative algorithms, proposed model gains more discriminative features and achieves competitive performance on terrain classification and segmentation tasks.

Polarimetric Target Detection in Compound Gaussian Sea Clutter With Inverse Gaussian Texture

This letter addresses the polarimetric target detection problem in compound Gaussian sea clutter. In view of the heavy-tailed characteristic of sea clutter, we model the sea clutter as compound Gaussian distribution with inverse Gaussian texture (IGCG). We propose three polarimetric compound Gaussian detectors based on the two-step Rao test, the Wald test, and the generalized likelihood ratio test (GLRT). Specifically, we assume that the polarimetric clutter covariance matrix (PCCM) and the inverse Gaussian textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we use the training data to estimate PCCM and maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) to estimate the inverse Gaussian textures, and we obtain the fully adaptive detectors. Then, we give proof of the constant false alarm rate (CFAR) properties of the proposed detectors. We validate the performance of the proposed polarimetric detectors by conducting experiments based on simulated and real sea clutter data. Finally, the numerical results indicate that the proposed detectors exhibit better detection performance than their competitors and are robust when the mismatched signal occurs.

Recognition of Autumn Crop Based on Polsar Data and Feature Selection

Optical remote sensed images have been intensively used to map global and regional agriculture information. However, few optical images could be collected due to cloud contamination during the crop growth period. Therefore, synthetic aperture radar (SAR) images could be used to extract crop distribution since it is capable of acquiring data without regard to bad weather conditions. Although numerous studies have been successfully carried out to highlight the potential of SAR images in crop monitoring, there are still some problems to be solved. The high dimensionality of multi-temporal SAR images remains a major issue, classification using all features is limited to efficiency. In this study, a method of autumn crop recognition based on PolSAR data and feature selection was proposed. Using Radarsat-2 PolSAR images acquired during the autumn crop growing season, the optimal subset of features was obtained by 3 feature selection methods and 15 polarimetric target decomposition methods. With optimal feature subset and train samples, crops and other ground objects were classified by SVM. The classification result was assessed by test samples.

Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs &lt;120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (PF) is fixed to 10−5, the probabilities of detection (PD) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (&lt;0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering &gt;120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the PF value was fixed to low detection probabilities 10−6 (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed PD values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors.

An investigation of the performances of polarimetric target decompositions using GB-SAR imaging

Ground-based synthetic aperture radar (GB-SAR) systems are mostly utilized to be practical practices in improved understanding of the complex mechanism of microwave backscattering. They also provide complementary information on evaluating the validity of the polarimetric analysis of air-borne or satellite-borne SAR applications. This study investigates some capabilities of polarimetric L-band GB-SAR imaging by testing its performance against a typical terrain and various kinds of manmade targets. Trihedral corner reflectors are also included in the analyses because of their importance in data calibration. Polarimetric backscattering signatures of different targets are analyzed in terms of qualitative assessment of amplitude images and identification and classification of scattering mechanisms through target decomposition techniques. The findings of these analyses and detailed discussions are presented. Specifically, the entropy/mean-alpha ((H/α ̅)) classification results are shown to be capable of clearly identifying the dominant scattering mechanisms occurring within the investigated scene.

Multi-View Feature Selection for PolSAR Image Classification via l₂,₁ Sparsity Regularization and Manifold Regularization.

Feature is a crucial element of polarimetric synthetic aperture radar (PolSAR) image classification. Multiple types of Features, such as polarimetric features (PF) generated from the PolSAR data and various polarimetric target decompositions, texture features (TF) of the Pauli color-coded PolSAR images are used as features for PolSAR image classification. The obtained PF and TF often form the high-dimensional data, which leads to high computational complexity. Moreover, some features are irrelative and do nothing to improve the classification performance. Therefore, it is fairly indispensable to select a subset of useful features for PolSAR image classification. This paper proposes a multi-view feature selection method for PolSAR image classification. Firstly, two types of features, PF and TF are generated separately. Then the optimization model is built to pursue the feature selection matrices. Specifically, in order to maintain the consistency of different types of features, we search for the common representation of multiple types of features in the optimization problem. The l2,1 norm sparsity regularization is imposed on the feature selection matrices to achieve feature selection. In addition, the manifold regularization on the common representation is utilized to preserve the structure information of the data. The effectiveness of the proposed method is evaluated on three real PolSAR data sets. Experimental results demonstrate the superiority of the proposed method.

Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset

Land cover classification with SAR images mainly focuses on the utilization of fully polarimetric SAR (PolSAR) images. The conventional task of PolSAR classification is single-pixel-based region-level classification using polarimetric target decomposition. In recent years, a large number of high-resolution SAR images have become available, most of which are single-polarization. This article explores the potential of object-level semantic segmentation of high-resolution single-pol SAR images, in particular tailored for the Gaofen-3 (GF-3) sensor. First, a well-annotated GF-3 segmentation dataset “FUSAR-Map” is presented for SAR semantic segmentation. It is based on four data sources: GF-3 single-pol SAR images, Google Earth optical remote sensing images, Google Earth digital maps, and building footprint vector data. It consists of 610 high-resolution GF-3 single-pol SAR images with the size of 1024 × 1024. Second, an encoder-decoder network based on transfer learning is employed to implement semantic segmentation of GF-3 SAR images. For the FUSAR-Map dataset, an optical image pretrained deep convolution neural network (DCNN) is fine-tuned with the SAR training dataset. Experiments on the FUSAR-Map dataset demonstrate the feasibility of object-level semantic segmentation with high-resolution GF-3 single-pol SAR images. Also, our algorithm obtains fourth place about the PolSAR image semantic segmentation on the “2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation.” The new dataset and the encoder-decoder network are intended as the benchmark data and baseline algorithm for further development of semantic segmentation with high-resolution SAR images. The FUSAR-Map and our algorithm are available at github.com/fudanxu/FUSAR-Map/.

Comparative analysis of different polarimetric target decompositions for Land cover Classification

Comparative analysis of different polarimetric target decompositions for Land cover Classification

Modified Tensor Distance-Based Multiview Spectral Embedding for PolSAR Land Cover Classification

This letter proposes a novel method for combining multiview features in polarimetric synthetic aperture radar (PolSAR) for land cover classification. It is well-known that feature extraction and classifier design are two significant steps in machine learning methods for PolSAR data interpretation. Each PolSAR pixel can be represented in different feature spaces, such as polarimetric data scattering, or the polarimetric target decomposition spaces. In this letter, a tensor-based multiview embedding algorithm is proposed to fuse those features from different spaces in order to obtain a distinctive set of features for the subsequent classification. Based on the pixel-based classification tasks, a modified tensor distance (MTD) is designed to accurately calculate the distance between tensors. It emphasizes the importance of the central pixel, and decreases the influence of the neighbors in the feature patch when calculating tensor distance. Furthermore, the complementary properties of different views are exploited by an MTD measured tensor multiview spectral embedding method, so as to obtain relevant low-dimensional features. Compared with state-of-the-art methods, the validation and effectiveness of the proposed method is demonstrated on two real PolSAR data sets.

Advanced Processing of 3D Computational Microwave Polarimetry Using a Near-Field Frequency-Diverse Antenna

Polarimetry is typically restricted to far-field characterization of a target using beam-like waves, which results in a 2 x 2 scattering matrix representation under two orthogonal in-plane polarization bases. However, a short-range (or radiative near-field) microwave polarimetric approach can recover a 3 x 3 polarimetric matrix representing a full vector polarimetric response of the imaged object. The computational imaging method retrieves this full polarimetric response by utilizing an ensemble of randomly polarized probing fields from a cavity-backed metasurface antenna as the enabling technology. In this paper, we describe the polarization states of the non-planar vector sensing fields with three-dimensional (3D) Jones vectors and examine the polarization diversity with the polarization ellipses in 3D space. Corresponding 3D polarimetric target parameters are derived from the 3D polarimetric matrix and the diagonalization process of this matrix. The generalized 3D target parameters disclose direct details of the imaged object which are otherwise inaccessible to the conventional 2 x 2 polarimetric scattering matrix description, especially the polarimetric features along the range direction. The target parameters reconstructed in experiments validate the effectiveness of our 3D polarimetric near-field imaging framework and the parameterization. The advanced processing and parameterization of 3D polarimetry indicate great potential applications in many short-range microwave imaging scenarios.

Soil Moisture Inversion Via Semiempirical and Machine Learning Methods With Full-Polarization Radarsat-2 and Polarimetric Target Decomposition Data: A Comparative Study

In this article, surface soil moisture was retrieved from Radarsat-2 and polarimetric target decomposition data by using semiempirical models and machine learning methods. The semiempirical models and machine learning techniques employed were Oh (1992), Dubois (1995), Oh (2004) and Generalized Regression Neural Network (GRNN), Least Squares - Support Vector Machine (LS-SVM), Extreme Learning Machine (ELM), Kernel based Extreme Learning Machine (KELM), Adaptive Network based Fuzzy Inference System (ANFIS), respectively. In addition, Yamaguchi, van Zyl, Freeman-Durden, H/A/α and Cloude polarimetric target decomposition methods were used in this study. For soil moisture inversion, firstly, preprocessing was applied to the Radarsat-2 image of two different dates with bare and moderately vegetated soil. Then, sigma nought coefficients and the polarimetric decomposition components were extracted as feature vector from preprocessed SAR image pixels corresponding to ground measured points. Lastly, sigma nought coefficients were used in semiempirical inversion models, and sigma nought coefficients and polarimetric decomposition components were used as input to machine learning methods. The best accuracy results for semiempirical models were 13.01 vol. % and 17.91 vol. % Root Mean Square Error (RMSE) for bare and moderately vegetated soil, respectively. The best accuracy for machine learning techniques were 4.04 vol. % and 2.72 vol. % RMSE for two dates, respectively. The results indicated that the machine learning techniques performed much better than the semiempirical models.

Entropy Based Target Identification in Synthetic Aperture Radar Polarimetry

Synthetic aperture radar is used for polarimetric target identification. It is most prominent imaging radar. This radar covers the widest ranges of earth crust with high resolution images. It captures images day and night. It is suitable for any seasonal weather conditions. The polarization data contains information, on scattering mechanism related to different objects. The objects are land, ocean, glaceries, snow and dense forest which are natural distributed targets. By the use of scattering mechanism the different objects are classified. Scattering mechanism is measured by scattering elements of the matrix. The full polarization of synthetic aperture radar data classifies the obtained image. This paper analyses an entropy based target identification related to synthetic aperture radar polarimetry. The method is also the outcome of Eigen decomposition analysis. The paper also gives broader view of identification of target using physical property and analytical model. The method is helpful for system level design and scattering process considerations.