Airborne Polarimetric Synthetic Aperture Radar Research Articles

Quality improvement synthetic aperture radar (SAR) images using compressive sensing (CS) with Moore-Penrose inverse (MPI) and prior from spatial variant apodization (SVA).

When the locations of non-zero samples are known, the Moore-Penrose inverse (MPI) can be used for the data recovery of compressive sensing (CS). First, the prior from the locations is used to shrink the measurement matrix in CS. Then the data can be recovered by using MPI with such shrinking matrix. We can also prove that the results of data recovery from the original CS and our MPI-based method are the same mathematically. Based on such finding, a novel sidelobe-reduction method for synthetic aperture radar (SAR) and Polarimetric SAR (POLSAR) images is studied. The aim of sidelobe reduction is to recover the samples within the mainlobes and suppress the ones within the sidelobes. In our study, prior from spatial variant apodization (SVA) is used to determine the locations of the mainlobes and the sidelobes, respectively. With CS, the mainlobe area can be well recovered. Samples within the sidelobe areas are also recovered using background fusion. Our method is suitable for acquired data with large sizes. The performance of the proposed algorithm is evaluated with acquired spaceborne SAR and air-borne POLSAR data. In our experiments, we use the 1m space-borne SAR data with the size of 10000 (samples) × 10000 (samples) and 0.3m POLSAR data with the size of 10000 (samples) × 26000 (samples) for sidelobe suppression. Furthermore, We also verified that, our method does not affect the polarization signatures. The effectiveness for the sidelobe suppression is qualitatively examined, and results were satisfactory.

Classification evaluation and improvement of airborne PolSAR images for land use mapping using deep learning

Polarimetric synthetic aperture radar (PolSAR) images have been widely used in many fields due to its advantage in obtaining full polarization information, especially in land use classification. To evaluate the performance of airborne PolSAR images in land use classification, this paper systematically evaluated the potential value of PolSAR images in land use classification by using machine learning and deep learning algorithms, and improved the classification performance of airborne PolSAR images by constructing a multi-structural feature aware attention network (MSFA-Net). The classification performance of different algorithms was evaluated by the overall accuracy. The results showed that the machine learning algorithm Random Forest (RF) produced the lowest overall accuracy of 71.93%. Using RF as the baseline accuracy, the deep learning-based classification algorithm significantly improved the classification accuracy. Specifically, Wide Contextual Residual Network achieved an overall accuracy improvement of 8.87% to 80.80%, and Two-branch CNN generated an overall accuracy of 84.49%. Multi-source CNN produced the second highest overall accuracy of 87.25%, an improvement of 15.32%. The proposed MSFA-Net algorithm achieved the highest classification accuracy of 89.29% with an improvement of 17.36%, which further improves the land use mapping capability of airborne PolSAR images. A moderate reduction in resolution helps to improve classification accuracy, with 84.36% accuracy after 4-fold downsampling compared with the original resolution accuracy of 69.30%. On the other hand, noise, polarization error and motion error had a negative impact on the classification performance. This paper highlights the application of airborne SAR images in land use classification, and strengthens the understanding of deep learning and the influence of different factors on PolSAR images in land use classification.

A Reflection Symmetric Target Extraction Method Based on Hypothesis Testing for PolSAR Calibration

Polarimetric calibration is indispensable to quantitatively apply and analyze the polarimetric synthetic aperture radar (PolSAR) image. At present, the polarimetric calibration methods relying on the assumption of reflection symmetry have been widely used, which need to extract the reference targets that meet the assumptions before calibration and then calculate the cross-pol channel imbalance and crosstalk errors. However, the distortion in the uncalibrated image will affect the calculation of polarization features, resulting in inaccurate target extraction results. Consequently, we proposed a reflection symmetric target extraction method that combines with spatial statistics information. The method first takes the initial extraction result based on the polarization power total Span and introduces the hypothesis testing to judge the homogeneous samples. Finally, we automatically calculate the threshold by the Otsu algorithm to achieve high-precision extraction of the reflection symmetric targets. Meanwhile, we carried out the polarimetric calibration experiments based on real C- and X-band airborne PolSAR data and conducted qualitative and quantitative evaluation and analysis of the experimental results. The studies demonstrated that, compared with classical approaches, the proposed approach further improved the accuracy of polarimetric calibration by extracting more accurate reference samples.

Semi-Supervised Classification of PolSAR Images Based on Co-Training of CNN and SVM with Limited Labeled Samples.

Recently, convolutional neural networks (CNNs) have shown significant advantages in the tasks of image classification; however, these usually require a large number of labeled samples for training. In practice, it is difficult and costly to obtain sufficient labeled samples of polarimetric synthetic aperture radar (PolSAR) images. To address this problem, we propose a novel semi-supervised classification method for PolSAR images in this paper, using the co-training of CNN and a support vector machine (SVM). In our co-training method, an eight-layer CNN with residual network (ResNet) architecture is designed as the primary classifier, and an SVM is used as the auxiliary classifier. In particular, the SVM is used to enhance the performance of our algorithm in the case of limited labeled samples. In our method, more and more pseudo-labeled samples are iteratively yielded for training through a two-stage co-training of CNN and SVM, which gradually improves the performance of the two classifiers. The trained CNN is employed as the final classifier due to its strong classification capability with enough samples. We carried out experiments on two C-band airborne PolSAR images acquired by the AIRSAR systems and an L-band spaceborne PolSAR image acquired by the GaoFen-3 system. The experimental results demonstrate that the proposed method can effectively integrate the complementary advantages of SVM and CNN, providing overall classification accuracy of more than 97%, 96% and 93% with limited labeled samples (10 samples per class) for the above three images, respectively, which is superior to the state-of-the-art semi-supervised methods for PolSAR image classification.

A NEW DISTRIBUTED TARGET EXTRACTION METHOD FOR POLARIMETRIC SAR CALIBRATION

Abstract. Polarimetric calibration is one of the preprocessing steps in the quantitative processing of Polarimetric synthetic aperture radar (PolSAR) data, and its accuracy will affect subsequent applications. At present, the polarimetric calibration method based on distributed targets is widely used, and this kind of method needs to extract distributed targets that satisfy certain scattering characteristics as the calibration reference ground object samples before calibrating. Therefore, the extraction accuracy of distributed targets has a great influence on the accuracy of polarimetric calibration methods based on such targets. Therefore, this paper proposes a new distribution target extraction method, which is based on the idea of KS hypothesis testing, and uses the homogeneity of the pixels in the window to determine whether it is a distribution target. To verify the effectiveness of the method, the X-band airborne PolSAR images are used as the data of the polarimetric calibration experiment. Experiments show that, compared with other extraction methods, our method can not only ensure the extraction accuracy of distributed targets, but also further improve the accuracy of polarimetric calibration.

Polarimetric Calibration and Spatio‐Temporal Polarimetric Distortion Analysis of UAVSAR PolSAR Data

AbstractThe polarimetric distortions present in the polarimetric SAR (PolSAR) data sets leads to unexpected results from the polarimetric decomposition and classification models, which in turn result in the ground target misinterpretation. The most important polarimetric distortions in airborne PolSAR data sets are the channel imbalance and crosstalk. The minimization of these polarimetric distortions using polarimetric calibration techniques is very important to carry out the quantitative analysis using PolSAR data and time series analysis and to compare results between different sensors. In the current state of the art, the spatial variability of the polarimetric distortions at different slant‐range positions in the data set was not completely analyzed and also the temporal variability of the polarimetric distortions using multiple data sets needs to be more investigated. Therefore, the main objectives of this study were to develop an operational processor to perform the external polarimetric calibration of the L‐band Uninhibited Aerial Vehicle Synthetic Aperture Radar system and to analyze the spatio‐temporal behavior of the polarimetric distortions. The polarimetric distortion matrix (PDM) was formulated after considering the crosstalk and channel imbalance polarimetric distortions to derive the scattering matrix free from all kinds of polarimetric distortions from the observed scattering matrix. The Quegan, improved Quegan, and Ainsworth methods for polarimetric calibration were implemented to solve the PDM. The residual channel imbalance and crosstalk after polarimetric calibration were estimated to compare the calibration efficiency of these methods. After that, a spatio‐temporal analysis was carried out with several data sets to assess the spatio‐temporal behavior of these polarimetric distortions. The polarimetric signatures and the coherency matrix behavior of the trihedral corner reflectors were used to analyze the ground target characterization improvement after polarimetric calibration. The results obtained from this study revealed that the crosstalk is the major cause for the polarimetric distortions and the channel imbalance and phase bias present in the data sets were not high. From the spatio‐temporal analysis, it was found that the variability of these polarimetric distortions both in spatial and temporal domains was minimum and can be discarded without causing any considerable error in the polarimetric calibration process.

Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

The road surface roughness is an important parameter that determines the quality of a road network. It has a direct influence on the grip and skid resistance of the vehicles. For this reason, this parameter has to be periodically monitored to keep track of its changes. Nowadays, road surface roughness is measured by driving measurement vehicles equipped with laser scanners all over the country. But, this approach is very costly, labor-intensive, and time-consuming. This article is done to evaluate the potential of high-resolution airborne polarimetric synthetic aperture radar (SAR) to remotely estimate the road surface roughness on a wide scale. Different SAR backscatter-based semi-empirical models and SAR polarimetry-based models for surface roughness estimation are implemented in this article. Also, a new semi-empirical model is proposed in this article, which is trained specifically for the road surface roughness estimation. Additive noise subtraction, upper sigma nought threshold masking, and lower signal-to-noise ratio threshold masking techniques were implemented in this article to improve the reliability of road surface roughness estimation. The feasibility of this approach is tested using fully polarimetric X-band datasets acquired with DLR's airborne radar sensor F-SAR. The surface roughness results estimated using these airborne SAR datasets show good agreement with the ground truth surface roughness values and the results are discussed in this article.

Winner Takes All: A Superpixel Aided Voting Algorithm for Training Unsupervised PolSAR CNN Classifiers

Unsupervised methods play an essential role in Polarimetric SAR (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this paper proposes a fully unsupervised pipeline for training CNNs. The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudo label generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob, while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne (ESAR/AIRSAR) and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with OA, AA, and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart,SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin, and even has comparable performance to the supervised CNN model (FCN).

Reflection of and vision for the decomposition algorithm development and application in earth observation studies using PolSAR technique and data

After reflecting on the past decomposition studies using the polarimetric synthetic aperture radar (PolSAR) technique and data in Earth observation (EO) studies, three primary issues are identified. Elements C12 and C32 of a covariance matrix, [C], are essential in the decomposition and cannot be ignored. Existing algorithms cannot adequately distinguish urban targets with large azimuth orientation angles from vegetation. The algorithms are complex in the formulation and procedure. To resolve the issues and envision future algorithm development, we have articulated three key modules. They are a separation factor to separate an azimuthally symmetric or asymmetric target, a diplane to model an asymmetric target in an urban area, and a procedure to derive an equivalent azimuth-orientation angle for the diplane. Then, a four-component decomposition algorithm was developed. The algorithm has been applied to multiple airborne and spaceborne PolSAR C- and L-band datasets covering areas in Canada, France, Morocco, and the USA. The primary radar target types included trihedral and dihedral corner reflectors (CRs), airport runway/taxiway, urban targets with azimuth-orientation angles ranging between 0° and 45°, ocean and inland water surfaces, city parks, grassland, forests, farmland, and desert. The separation factor delineates a symmetric or asymmetric target at a correct average rate of 92.7%. The diplane coupled with the derived equivalent azimuth-orientation angles correctly modeled radar backscatter from dihedral CRs and urban asymmetric targets. The algorithm delineated each type of ground target with an appropriate and dominant single, double, or volume scattering. Furthermore, the algorithm has four readily interpretable components, and its mathematical expression is not complicated. Therefore, the primary objectives to resolve the above three issues and to have an algorithm with well-balanced usability in EO studies and complexity in formulation and procedure are achieved.

Polarimetric calibration of spaceborne and airborne multifrequency SAR data for scattering-based characterization of manmade and natural features

The Polarimetric Synthetic Aperture Radar (PolSAR) systems use electromagnetic radiations of different polarizations in the microwave frequency to collect the scattering information from targets on the Earth. Nevertheless, as with any other electronic device, the PolSAR systems are also not ideal and subjected to distortions. The most important of these distortions are the polarimetric distortions caused due to the channel imbalance, phase bias, and crosstalk between the different polarization channels. For the spaceborne PolSAR systems, the Earth’s ionosphere also contributes to an additional polarimetric distortion known as the Faraday rotation. An effort was made in this study to perform the polarimetric calibration of the Quad-pol and Compact-pol PolSAR datasets acquired using different airborne and spaceborne PolSAR systems to estimate and minimize these polarimetric distortions. The investigation was also done to analyze the impact of these polarimetric distortions on the scattering mechanisms from ground targets and on its dependency on the radar wavelength. The study was done using the UAVSAR L-band Quad-pol dataset, RADARSAT-2 Quad-pol dataset, ALOS-2 PALSAR-2, ISRO's L&S- Band Airborne SAR (LS-ASAR) Quad-pol and Compact-pol datasets, and the RISAT-1 Compact-pol dataset. Calibration of the airborne PolSAR data was carried to understand the level of polarimetric distortions in the LS-ASAR product that is a precursor mission to the spaceborne Dual-Frequency L&S Band NASA-ISRO Synthetic Aperture Radar (NISAR) mission. It is understood that the crosstalk is the dominant polarimetric distortion, which severely affects the PolSAR datasets compared to the other polarimetric distortions, and it is more for the higher wavelength PolSAR systems. The Quegan, Improved Quegan, and Ainsworth algorithms for crosstalk estimation and minimization was performed for the different Quad-pol datasets and it was found that the Improved Quegan algorithm is suitable for removing crosstalk from datasets having high crosstalk and the Ainsworth algorithm is suitable for removing crosstalk from datasets having low crosstalk. The Freeman method of the polarimetric calibration was implemented for the compact-pol datasets and it was able to considerably minimize the polarimetric distortions. The coherency matrix, scattering matrix, model-based decomposition, polarimetric signatures, and roll invariant parameter-based analysis revealed that all the datasets after polarimetric calibration were showing the correct scattering responses expected from the ground targets.

Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data

This study aimed at evaluating the potential of machine learning (ML) for estimating forest biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two different machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and LiDAR measurements, were made available by the European Space Agency (ESA) in the framework of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR measurements at all polarizations to the target biomass was evaluated on the entire set of data from all the campaigns, and separately on the dataset of each campaign. Based on the results of the sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data and the corresponding local incidence angles, and output is the estimated biomass. To allow the comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the available dataset, and validated on the remaining part of the dataset. The validation of the algorithms demonstrated that both machine-learning methods were able to estimate the forest biomass with comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation coefficient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible, while the specific algorithms obtained 0.22 ≤ R ≤ 0.92 and 19 ≤ RMSE ≤ 70 (t/ha). The study also pointed out that the computational cost is similar for both methods. In this respect, the training is the only time-demanding part, while applying the trained algorithm to the validation set or to any other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were applied to the available SAR images for obtaining biomass maps from the available SAR images.

Speckle Suppression in Multi-Channel Coherent Imaging: A Tractable Bayesian Approach

Coherent imaging methods like synthetic aperture radar (SAR) are subject to speckle, and the suppression of this noise-like quality is often considered a prerequisite to image interpretation. Likewise, circumstances such as frequency jamming or system multiplexing produce gaps in the data and corresponding image artifacts, which further impact the utility of resulting imagery and induce correlation in the speckle process. For multi-channel SAR configurations such as the polarimetric or interferometric modes, a Bayesian imaging procedure is proposed to harvest the benefits of jointly processing channels in order to recover scattering information. The inference technique recovers the per-pixel multi-channel SAR covariance and incorporates a statistical model of speckle and a priori knowledge of the varieties of clutter present in the scene. Further, by combining speckle reduction into the image formation process for gapped aperture measurements, the estimator can balance competing objectives of image resolution, side-lobe suppression, texture preservation, and speckle reduction. An expectation-maximization algorithm is made computationally tractable by a graph-coloring probing technique to provide the block-diagonal portion of a large matrix inverse. Recovery results with simulated and measured fully polarimetric airborne SAR data indicate that the proposed method reduces the appearance of speckle and preserves spatial resolution.

SPICE-Based SAR Tomography over Forest Areas Using a Small Number of P-Band Airborne F-SAR Images Characterized by Non-Uniformly Distributed Baselines

Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas. SPICE is a sparse spectral estimation method that achieves a high vertical resolution, and takes account of the noise adaptively for each resolution cell. Thus, it does not require the user to select a hyperparameter. Furthermore, the used sparse basis not only ensures the sparsity of the forest canopy scattering contribution, but it can also keep the original sparse information of the ground contribution. The proposed method was tested in simulated experiments and the results demonstrated that W&O-SPICE can successfully reconstruct the vertical structure of a forest. Moreover, three P-band fully polarimetric airborne SAR images with non-uniformly distributed baselines were applied to reconstruct the vertical structure of a tropical forest in Mabounie, Gabon. The underlying topography and forest height were estimated, and the root-mean-square errors (RMSEs) were 6.40 m and 4.50 m with respect to the LiDAR digital terrain model (DTM) and canopy height model (CHM), respectively. In addition, W&O-SPICE showed a better performance than W&O-CS, beamforming, Capon, and the iterative adaptive approach (IAA).

Scattering Characterization of Obliquely Oriented Buildings from PolSAR Data Using Eigenvalue-Related Model

Scattering characterization of obliquely oriented buildings (OOBs) from polarimetric synthetic aperture radar (PolSAR) data is challenging since the general double-bounce scattering does not support their dominant scattering mechanism. In this paper, a physical scattering model combining the eigenvalues of coherency matrix is proposed to characterize the scattering of OOBs. The coherency matrix is first operated by eigenvalue decomposition and a refined OOB descriptor is presented based on these eigenvalues. Considering the actual proportions of co-polarization and cross-polarization components, the descriptor is then adopted to modify the matrix elements of the well-known cross scattering model, thus introducing the OOB scattering model. Finally, strategies of model parameter solution are designed and the involved decomposition is complete accordingly. The proposed method is tested on spaceborne and airborne PolSAR data and the results confirm its effectiveness, which clearly call for further research and application.

Co-polarization channel imbalance phase estimation by corner-reflector-like targets

Polarimetric calibration is a critical step to suppress the potential system distortion before implementing any applications for polarimetric synthetic aperture radar (PolSAR). Among all the distortion elements, the crosstalk and cross-pol channel imbalance are generally estimated by the use of natural media, and the co-pol channel imbalance is traditionally solved by the use of corner reflectors (CRs). However, the deployment of ground CRs is costly and may even be impossible in some areas. Many bright point targets, such as poles, lamps, and corner points of structures, are commonly found in manmade regions. In particular, if the object orientation is parallel or perpendicular to the radar flight direction, some points will present similar polarimetric responses to trihedral or dihedral CRs. These points, which are referred to here as “CR-like targets”, can be treated as a supplement to approximately solve the system distortion elements when CRs are unavailable. In this paper, we propose a novel step-by-step algorithm to determine the CR-like targets and estimate the co-pol channel imbalance phase in uncalibrated PolSAR imagery. Chinese X-band airborne and C-band satellite PolSAR data were used to test the proposed method. Compared with the CR-derived co-pol channel imbalance phase, the solution errors of the CR-like targets were 1.305° and 0.03° for the X- and C-band experiments, respectively. The results of the experiments confirm that the solutions of the CR-like targets are very close to those of ground-deployed CRs, and the proposed method can be considered as an effective way to calibrate PolSAR images when sufficient CR-like point targets are detected in manmade regions.

A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height.

Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.

Multichannel simultaneous dual-band fully polarimetric airborne synthetic aperture radar: system features and experimental results

This paper presents a frequency modulated continuous wave (FMCW) technology-based airborne synthetic aperture radar (SAR) system. This radar system operates on an eight-channel simultaneous dual band (X- and L-bands) in fully polarimetric mode. Compared with the traditional multiband airborne SAR, this radar system features a small size and low power consumption based on the FMCW technology. The antenna subsystem can be easily mounted behind the cargo door of a small aircraft platform without any modifications to the aircraft structure. A Cessna 208 is employed to mount this radar system in a left side looking installation. A single pass flight test is carried out in China, and preliminary imaging results are acquired and classified. The merged X–L band image provides the highest classification accuracy compared with the single X- and L-bands and optical images. The experimental results indicate that the proposed FMCW SAR is a powerful tool for terrain classification, especially for rural-urban region classification.

Random Similarity-Based Entropy/Alpha Classification of PolSAR Data

A fast and competent alternative to the widely used Cloude–Pottier entropy/alpha ( $H/ \alpha $ ) classification is developed for the rapid response application of polarimetric synthetic aperture radar (PolSAR) data. Random similarity which measures both the scattering similarity and randomness of polarimetric scatterers is used to enable an $H/ \alpha $ -like classification in terms of two key parameters, i.e., the similarity-based angle $\alpha _{s}$ and entropy $H_{s}$ , as the alternatives to the Cloude–Pottier angle $\alpha $ and entropy H , respectively. Parameters $\alpha _{s}$ and $H_{s}$ maintain the same physical information as parameters $\alpha $ and H , so the existing knowledge regarding $\alpha $ and H can be naturally extended to them. Angle $\alpha _{s}$ measures scattering mechanism and is ranged within the same interval [0°, 90°] as $\alpha $ while entropy $H_{s}$ measures scattering randomness which is also a logarithm within the interval [0, 1] similar to H . The pixelwise eigendecomposition in the calculation of $\alpha $ and H is avoided for $\alpha _{s}$ and $H_{s}$ , and the resulted efficiency improvement is, thus, considerable. By rigorously modeling the $\alpha _{s}\hbox{--}\alpha $ and the $H_{s}\hbox{--}H$ relationship to illustrate the competence of the $H_{s}\hbox{--}\alpha _{s}$ combination in discrimination of target and to identify the searching ranges for the boundary determination, an $H_{s}/ \alpha _{s}$ classification is then devised with the boundaries of the eight effective classes being determined by an optimization to minimize the misclassification and further integrated on different PolSAR images to remove the possible bias from dataset for general applicability. Comparative experiment on both space-borne and airborne PolSAR datasets with $H/ \alpha $ indicates that $H_{s}/ \alpha _{s}$ can achieve very consistent roll-invariant target discrimination as $H/ \alpha $ (overall accuracy ≥95%, kappa coefficient ≥0.95) but with averagely 150 times higher efficiency although the LAPACK-based eigenanalysis tool has been used to accelerate the eigendecomposition for $H/ \alpha $ . Preliminary result from the adaptive model-based classification reveals that the $H_{s}$ -involved boundaries in $H_{s}/ \alpha _{s}$ are independent of a particular PolSAR dataset.

Improved airborne PolSAR calibration algorithm based on time-variant attitude compensation

The polarimetric synthetic aperture radar (PolSAR) usually has to be calibrated before practical application, so as to compensate for polarimetric distortion. The varying platform attitude is one of the factors causing distortion but has rarely been considered in existing polarimetric calibration algorithms. With the resolution of PolSAR systems improving and the synthetic aperture time prolonging, this factor cannot simply be ignored. The varying attitude will distort the polarimetric information by rotating the polarimetric orientation angle, and such distortion changes with azimuth time. In this article, we modified the conventional polarimetric system model to take account of the time-variant impact of the unstable platform attitude. A calibration algorithm is proposed to compensate the time-variant attitude impact on the raw return data. The proposed calibration algorithm is tested on the data collected by Institute of Electronics, Chinese Academy of Sciences P-band PolSAR system. Results show that it can achieve better performance by reducing crosstalk error than two conventional methods.

Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints

Spectral clustering is a very popular approach which has been successfully used in unsupervised classification of polarimetric synthetic aperture radar (PolSAR) imagery. However, due to its high computational complexity, spectral clustering can only be applied to small data sets. This article provides a framework for spectral clustering of large-scale PolSAR data. As computing and processing the pairwise-based affinity matrix is the bottleneck of the spectral clustering approach, we first introduce a representative points-based scheme in which a memory-saving and computationally tractable affinity matrix is designed. The subsequent spectral analysis can be solved efficiently. Second, a simple one-parameter superpixel algorithm is introduced to generate representative points. Through these superpixels, spatial constraints are also naturally integrated into the classification framework. We test the proposed approach on both airborne and space-borne PolSAR images. Experimental results demonstrate its effectiveness.