Polarimetric Target Decomposition 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.

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.

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.

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.

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.

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.

PolSAR Feature Extraction Via Tensor Embedding Framework for Land Cover Classification

Polarimetric synthetic aperture radar (PolSAR) as a typical multi-channel sensor can obtain refined geometrical and geophysical information. In the PolSAR land cover classification task, feature extraction is regarded as a critical step for the final classification. It can employ multi-modal features from the original scattering data, polarimetric target decomposition, and other transformation space. Then, how to efficiently combine multi-modal polarimetric information and extract discriminant features is an important challenge for PolSAR image processing. Graph embedding methods have become a significant technique to deal with feature extraction and dimensionality reduction (DR) problems in recent years. It provides a unified linearization framework in machine learning and other pattern recognition tasks. In this article, an extended tensor embedding framework is introduced to extract the intrinsic features for PolSAR land cover classification. First, each pixel is represented by a feature cube that is constructed by groups of polarimetric scattering signals and target decomposition features in a fixed size patch. Second, an intrinsic matrix is constructed to describe the original geometrical and statistical properties of the samples, and a penalty matrix is designed to represent some constraints. Third, the vector-based algorithms are transformed into tensor space in an unified framework and based on the pair of matrices to obtain the projection matrices in each mode by an iterative optimization process. The effectiveness of the proposed methods is demonstrated on three RADARSAT2 data sets covering the regions of Xi’an, San Francisco, and Flevoland, respectively. The visualization and quantification results show that the proposed method has superiority in land cover classification.

PolSAR image classification based on deep polarimetric feature and contextual information

Polarimetric synthetic aperture radar (PolSAR) image classification is a challenging task due to lack of effective feature representation approaches. However, when it comes to deep feature representation, there is little research that pays enough attention to make full use of existing expert knowledge. We propose a model for deep polarimetric feature extraction, and a superpixel map is used to integrate contextual information. The proposed model uses multiple polarimetric algebra operations, polarimetric target decomposition methods, and convolutional neural network (CNN) to extract deep polarimetric features. The core idea is to utilize expert knowledge of the target scattering mechanism interpretation to assist the CNN classifier in feature extraction and employ superpixel algorithm based on Wishart distribution to improve the final classification performance. The proposed method is able to get abstract and discriminative representation from initial features, achieving robust and improved performance for PolSAR images. Compared with other state-of-the-art methods, experiments on the classification of three real PolSAR datasets are performed to demonstrate the superiority of the proposed approach.

Model-Based Polarimetric Decomposition With Higher Order Statistics

This letter presents a new general framework for solving polarimetric target decompositions that extends them to use more statistical information and include radar texture models. Polarimetric target decomposition methods generally have more physical parameters than equations and are, thus, underdetermined and have no unique solution. The common approach to solve them is to make certain assumptions, thus fixing some parameters, allowing the other parameters to be solved freely. This letter explains how to obtain additional equations from several statistical moments to find unique solutions and to address the issue of textured product models. The current work extends our previous conference works [1] – [3] . Preliminary results are demonstrated for a well-known real polarimetric synthetic aperture radar scene for the three-component Freeman–Durden decomposition.

Retrieval of Polarimetric Azimuthal Angular Characteristics via the Application of Target Decomposition to Spectral Domain Circular SAR Images

In this paper, we discuss the application of polarimetric target decomposition (TD) theorems to circular synthetic aperture radar (CSAR) images to retrieve the polarimetric azimuthal angular characteristics of the targets. In CSAR systems, radar-carrying aircraft moves along a circular path. The antenna beam is pointed toward the center of the circular path during the data acquisition to irradiate the spotlighted area from various azimuthal or aspect angles. Therefore, the resultant polarimetric CSAR images contain information about the polarimetric scattering mechanisms of the targets at each aspect angles. To recover this information, we propose the use of spectral decomposition of CSAR images in combination with several TD algorithms. The spectral domain CSAR images can be viewed as the azimuthal angular spectrum images. Thus, the application of TD algorithms to the spectral domain images is expected to be able to obtain the angular dependence and polarimetric scattering properties of the targets simultaneously. We carried out a simple numerical simulation and an indoor laboratory experiment to validate the proposed TD scheme.

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.