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In this letter, we present a novel ship detection method for polarimetric synthetic aperture radar (PolSAR) images. Generalized polarization relative entropy (GPRE) is proposed to measure the differences between the target and clutter in scattering mechanism, randomness, and intensity. Since it is difficult to derive a theoretical closed-form of the GPRE, we employ the kernel density estimation to model the distribution of the GPRE in ocean regions. Then, a constant false alarm rate (CFAR) ship detection method is proposed based on the estimated distribution. Experiments performed on both synthetic and real scene images demonstrate the effectiveness of the proposed method.

To date, several different methods for polarimetric synthetic aperture radar (SAR) calibration with distributed targets have been proposed in the literature. The basic assumptions on the distributed target that are used by these methods are almost identical. Their difference is about the assumptions on noise. In this article, the research shows that the subtle difference between the assumptions on noise leads to two different calibration models (CMs), which is the primary cause of the differences between various methods. According to the used CM, the methods in the literature can be categorized into two groups. Because this article focuses on the CMs, thus the optimal estimator in each group is used for comparison so as to exclude the impacts of different parameter estimation algorithms. The results suggest that neither of the optimal estimators is always superior to the other. In practice, we cannot determine which estimator is better, so we recommend using the mean value of the two optimal estimators (i.e., $ { \widehat {\boldsymbol \varphi }}^{\star } $ ) for calibration because it was proved to be (at least) better than the worse one. In the research, the signal-to-noise ratio (SNR) was proved to be a proper indicator for assessing whether ${ \widehat {\boldsymbol \varphi }}^{\star } $ is reliable. Hence, an estimator for the mean SNR is proposed. In this article, some simulation experiments are used to verify some critical conclusions that we have drawn. The practical use of ${ \widehat {\boldsymbol \varphi }}^{\star } $ and an assessment of its reliability with the estimated mean SNR are illustrated with DLR E-SAR data from the 2006 AgriSAR campaign.

Many polarimetric features, including decomposition components, can be extracted from polarimetric synthetic aperture radar (PolSAR) data. The polarimetric features usually reflect the physical mechanisms of ground targets and play an important role in PolSAR image classification. However, the feature values may vary largely due to the differences in system parameters of PolSAR sensors, which result in that the trained classifiers on sample data from one source PolSAR image scene may perform poorly in another source PolSAR image scene. The direct use of polarimetric features can produce wrong identifications. In this letter, we mainly deal with the components extracted by different decomposition methods and proposed a simple but efficient component ratio-based distance (CRD), which is an intracross-component distance, in contrast with component-to-component distances. The combinations with $\mathcal {L}_{1}$ distance and $\chi ^{2}$ distance can generate $\mathcal {L}_{1}$ -CRD and $\chi ^{2}$ -CRD and benefit from their robustness to small values. CRDs capture correlations between scattering components with only a linear computational complexity. Finally, we replace the distance measurement in k-nearest neighbor (KNN) with CRDs and employ the improved classifiers to classify PolSAR images. Based on the ratios of scattering components, CRD can also be used for cross-source PolSAR images, ignoring the differences in sensors, acquired time, imaging scenes, and even wavebands. Preliminary experiments on real PolSAR data sets demonstrate promising results of CRDs for image classification.

Polarimetric synthetic aperture radar (PolSAR) image classification has been widely applied in many fields, such as agriculture, meteorology and military. However, some problems, such as the deficiency of labeled data and the underutilization of data information, are always the challenges that can not be ignored in PolSAR image classification. In this paper, a semi-supervised complex-valued generative adversarial networks (SSCV-GANs) is proposed for the first time to address the two issues mentioned above simultaneously. On the one hand, the complex-valued model conforms with the physical mechanism of PolSAR data and it plays an important role for retaining and utilizing amplitude and phase information of PolSAR data. On the other hand, we also present a new complex-valued GANs together with semi-supervised learning to alleviate the problem of insufficient labeled data. Specifically, our complex-valued GANs expands the training data set by generating fake data. Flevoland data and San Francisco data are used to validate the effectiveness of our model. Experimental results show that our model outperforms existing state-of-the-art models in terms of classification accuracy, especially for conditions with fewer labeled data. In particular, the analysis of the statistical distribution of the generated fake data and the real data further demonstrate the effectiveness of the proposed SSCV-GANs.

This article presents a method for extracting the relative polarimetric distortion parameter (PDP) values of polarimetric radar systems, including polarimetric synthetic aperture radar (PolSAR). By using one trihedral and two dihedrals and ignoring the high-order terms of the channel crosstalk, the PDPs of a PolSAR system can be estimated efficiently and straightforwardly without the need for iterative procedures or the system reciprocity assumption. Most importantly, the applicability issues are analyzed to obtain accurate PDP values. A sensitivity analysis of the target orientation angle (TOA, a rotation about the radar line of sight) error $\Delta \boldsymbol{\theta }$ is theoretically analyzed and experimentally studied. It appears that the impacts of $\Delta \boldsymbol{\theta }$ of different known targets on the channel imbalances and channel crosstalk estimation can be relatively independent and distinguished using our first-order approximate method. Using the proposed method, the channel imbalances are first estimated using the trihedral and 45° dihedral corner reflectors. A 22.5° dihedral should be involved to solve the sign ambiguity. The TOA error of the 45° dihedrals is not significantly sensitive to the channel imbalance estimation. Then, the channel crosstalk can be estimated using the trihedral and 0° dihedral corner reflectors. The channel crosstalk estimation in this step is relatively sensitive to the TOA error, which should be reduced to an acceptable level. Limitations due to the radar cross-section issues of the corner reflectors and the strength of the original channel crosstalk are also analyzed. Finally, the results of simulations and practical N-SAR experiments show the effectiveness of our proposed algorithm.

In this letter, we present a new matrix formulation of range and azimuth ambiguities in polarimetric synthetic aperture radar. We consider the general hybrid case, where the scattering matrix is measured by transmitting a general orthogonal elliptical pair as interlaced pulse trains with simultaneous linear H and V receive. Earlier papers have shown some potential advantages for ambiguity suppression in this mode, and here, we confirm and expand these and show two key new ideas: first that successful suppression of range ambiguities is strongly phase dependent and second that suppression can be achieved for a set of elliptical polarizations along a great circle of the Poincare sphere

This paper proposes a novel data filtering algorithm for fully polarimetric synthetic aperture radar (PolSAR) based on the degree of polarization (DoP) information. First, we define the homogeneity degree and polarization independence degree using the DoP information, and propose a feature plane to characterize the target feature. Second, employing the feature plane, we categorize the targets into three types and assign specific filtering policy for each type to estimate the optimal filtering window sizes. Finally, the $T$ -matrices of fully PolSAR data are filtered using the windows with estimated optimal sizes. Compared with boxcar filter, refined Lee filter, scattering model-based filter, and improved sigma filter in processing ALOS2-PALSAR2 data, the proposed DoP-based algorithm presents the best filtering performance.

The cross-polarized scattering (HV) of polarimetric synthetic aperture radar (PolSAR) data is caused not only by forest but also by urban buildings with azimuth orientation angles. Since the general double scattering in model-based decomposition does not support their dominant scattering mechanism, it's still a challenge for modeling the scattering mechanism of oriented buildings. In this paper, the HV induced by oriented buildings is modeled by a rotated dihedral corner reflector. The cross scattering matrix of oriented building is obtained by averaging a cosine squared distribution with its peak at the dominant polarimetric orientation angle (DPOA) of an area and an adaptive width. The relation between DPOA and distribution width is acquired. Then an optimization strategy which eliminates the negative power and balances time and efficiency is proposed to estimate the scattering contributions. The proposed algorithm is tested on AIRSAR data of San Francisco and UAVSAR of San Diego and the results confirm the effectiveness.

An adaptive classification is developed as a hybrid of the eigenvector-based and the model-based target decompositions for polarimetric synthetic aperture radar (PolSAR) data. The classification adopts the canonical scattering models that widely used in model-based decompositions to provide an improvement for the well-known ${H/\alpha }$ classification. First, a correspondence principle is adopted to adaptively identify the matched canonical models. The selected models are parallelly combined based on the scattering similarity for a fine depiction of the scattering mechanism then. Twelve classes are finally obtained, and each one carries a unique symbol to show a specific scattering. The classification does not depend on a particular data set, avoids the hard partitioning, and solves the obscures in ${H/\alpha }$ . Comparison on the real PolSAR data sets with ${H/\alpha }$ and the existing scattering similarity-based classification validates the better discrimination.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2018-03-22, 資料番号: PA1810018000

Polarimetric synthetic aperture radar (PolSAR) data show good performance in near-real-time earthquake/tsunami damage assessment. In this paper, an improved damage level index for earthquake/tsunami damage level mapping of urban areas based on distance metric learning using full PolSAR data has been proposed. First, urban areas are extracted by our proposed classification method, which conjunctively uses multiresolution segmentation and support vector machine. Then, a double-bounce scattering power-based damage level index is adopted for damage level mapping. To solve the problem that the primary result does not match with the truth damage level in heavily damaged areas, distance metric learning method is introduced to calculate the Mahalanobis metric to compose an improved index. Extensive experimental comparison and analysis on L -band ALOS PALSAR data of Tohoku earthquake demonstrate the effectiveness of the proposed index. Both the analysis about damage level assessment map and sample areas’ linear fitting with truth damage level indicate the improved damage level index achieves high accuracy. The same analysis was applied to evaluate the damage induced in Kumamoto earthquake event. The analysis result verified the robustness of the proposed damage level index.

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.

In this paper, a theoretical and experimental analysis of polarimetric synthetic aperture radar (SAR) architectures is undertaken for sea oil slick observation purposes. Reference is made to the conventional full-polarimetric (FP) SAR that is here contrasted with new-generation polarimetric SAR architectures, known as compact-polarimetric (CP) SAR. Two CP modes are considered, i.e., the hybrid-polarity and π/4 modes, whose measurements are emulated from actual L- and C-band FP SAR data. Polarimetric sea surface scattering is predicted according to an extended version of the Bragg scattering model (X-Bragg) in order to point out the differences exhibited between FP and CP SAR architectures and among CP SAR modes. Theoretical predictions are then contrasted with experiments undertaken on actual polarimetric SAR data collected over well-known oil slicks and weak-damping surfactants. Results confirm model prediction, showing that differences mainly apply when polarimetric features are estimated over slick-free sea surface using different SAR architectures, with the π/4 mode behaving closer to FP SAR. Although CP SAR architectures measure only a subset of the FP information content, they represent an interesting operational alternative for both detecting oil slicks and discriminating them from weak-damping surfactants.

Polarimetric synthetic aperture radar (PolSAR) images are extensively used for land-use/land-cover (LULC) classification. One of the important issues in radar remote sensing is urban area detection, where difficulties are found because of its heterogeneity. In this paper, we are interested in urban area detection using PolSAR images which allow us detecting the scattering mechanisms by the use of polarimetric target decompositions methods. We propose in this paper two methods: in the first one, we use the powers of Yamaguchi four-component decomposition and in the second method, we use the coefficients of PolSAR covariance matrix calculated in the circular polarization basis. We added in each method the complex Wishart maximum likelihood (ML) classifier to refine the classification results. To validate both methods, we used two PolSAR images acquired in C-band by RADARSAT-2 satellite over the El Hamiz city in Algeria and San Francisco Bay. The two proposed algorithms give accurate results in both test sites, with superiority of the circular condition method.

In this paper, an algorithm for estimating urban density from polarimetric synthetic aperture radar (SAR) images is proposed. Polarization orientation angle (POA) and four power components derived by four-component decomposition are used in the algorithm. In particular, in urban areas, SAR data are generally affected by factors such as the interval between buildings, building height, and building azimuth angle. Here, building azimuth (orientation) angle means the relative azimuth between the wall normal and the radar's ground range direction. The interval between buildings and building height are used for building density calculation such as the building-to-land ratio and the floor area ratio. However, building azimuth angle which depends on satellite orbit has almost no relation with building density. The scattering intensity of microwaves emitted from SAR has a strong dependence on this building azimuth angle. Therefore, the main part of this paper is focused on the correction of this angular effect. The first step in the POA correction method is the extraction of homogeneous-POA city districts. In the second step, each power component's scattering intensity is normalized for all pixels in a particular POA interval separately for different POA types of districts. In the case of Tokyo metropolitan area, Japan, estimated urban density from ALOS/PALSAR data has correlation coefficients of nearly 0.7 with the building-to-land ratio and 0.5 with the floor area ratio on the scale of hundreds of meter. In the areas where strong POA dependence is seen, the improvement of the correlation coefficient runs up to approximately 0.2.

The earthquake and tsunami of March 11, 2011 killed more than 15 000 people in Eastern Japan. The importance of remote sensing in understanding the damage caused by natural disasters is quite significant, and many data sets were acquired after the events. In this paper, we demonstrate the importance and the potential of full polarimetric synthetic aperture radar (SAR) images for damage assessment. Full polarimetric SAR images acquired by the spaceborne ALOS/PALSAR system from the Japan Aerospace Exploration Agency (JAXA) on November 21, 2010 and April 8, 2011 and acquired by the airborne Pi-SAR2 system from the National Institute of Information and Communications Technology (NICT) on March 12 and 18, 2011 are used for this analysis. Model-based decomposition is applied and clearly shows the scattering mechanism changes at the seriously damaged downtown of Ishinomaki city and the flooded areas near the main stream of the Kitakami River. Polarization orientation angle is estimated to provide additional information to understand the damage effect in the built-up areas. Eigenvalue-eigenvector-based decomposition analysis is also employed to further confirm the scattering mechanism changes of the flooded areas. ALOS/PALSAR does not have fine enough resolution; however, the difference of the scattering mechanisms is sufficient to identify the damaged and flooded areas. In addition, the Pi-SAR2 data sets are used to analyze the flooded paddy fields in Natori city. The relative backscattering values are compared with the multitemporal images and the cross-polarization component (HV) is observed to be more sensitive to the flooded boundary. The automatically detected flooding maps using the cross-polarization component were found to provide relatively accurate results.

Large-scale earthquakes severely damage people's lives and properties. Airborne and spaceborne remote sensing can be used accurately and effectively to monitor and assess earthquake disasters in near real time, providing an important scientific basis and decision-making support for government emergency command and postdisaster reconstruction. This letter proposes a new H-α-ρ method (H is the entropy, α is the average scattering mechanism, and ρ is the circular polarization correlation coefficient) for extracting the spatial distribution of collapsed buildings using RADARSAT-2 fine-mode polarimetric synthetic aperture radar (SAR) data. The method was tested on imagery from the Yushu earthquake in the Qinghai Province of China which occurred in 2010. When compared with high-resolution optical imagery, the result indicates that the proposed method can be effectively used to identify collapsed buildings and also demonstrates that polarimetric SAR data have potential for application in urban disaster monitoring and assessment.

Recent studies have shown that the volumetric scatterers of different canopy types warranted varying scattering models, which feature different orientation and shape characteristics. In this paper, we explore the possibility of estimating the orientation and shape parameters from POLSAR observations. Both the orientation and shape parameters of the canopy are defined by a cloud of spheroids with independent orientation and shape distributions. The respective parameters clearly express themselves in the circular polarization basis in which we derive the analytic solutions. Applying the estimation to POLSAR data, we demonstrate these parameters capable of characterizing the canopy scatterers, providing a consistent physical interpretation of the target scattering mechanisms and generating a fine-grained initialization for Wishart classifications of vegetation and forest targets.

The use of a low profile, light weight and easy to fabricate transpolarising surface placed on one side of a trihedral corner reflector (TCR) as polarimetric calibrator is presented in this letter. The transpolarising-TCR (TTCR) presents a high backscattered cross-polar response contrary to standard TCRs. The performance of this device has been tested at X-band using the UPC GB-SAR., Comment: Accepted for publication in IEEE Geoscience and Remote Sensing Letters

This paper compares two well-known polarimetric decomposition theorems, Cloude-Pottier and Freeman-Durden, applied to L- and C-band Airborne Polarimetric Synthetic Aperture Radar (AIRSAR-POLSAR) data acquired during the Cold-Land Processes Field Experiments. Three field campaigns were carried out in February 2002, March 2002, and March 2003 over a snow-covered open terrain, a sparse coniferous forest, and a dense coniferous forest. The analysis evaluates the ability of the two target decomposition methods for the identification and understanding of the main scattering mechanisms.

A study on sea oil spill observation by means of polarimetric synthetic aperture radar (SAR) data is accomplished. It is based on the use of a polarimetric constant false alarm rate filter to detect dark patches over SAR images. Then, the target decomposition theorem is exploited to distinguish oil spills and look-alikes. Experiments are conducted on polarimetric SAR data acquired during the SIR-C/X-SAR mission on October 1994. The data were processed and calibrated at the Jet Propulsion Laboratory, National Aeronautics and Space Administration. Results show that the new polarimetric approach is able to assist classification

The symmetric scattering characterization method (SSCM) has been recently introduced for high-resolution characterization of certain targets under coherent conditions. SSCM is based on the Poincare/spl acute/ sphere representation, which supports a high-resolution decomposition of symmetric target scattering, as well as assessment and validation of the backscatter coherence. In this paper, the SSCM is investigated for ship characterization using Convair-580 polarimetric synthetic aperture radar (SAR) data. It is shown that the target Poincare/spl acute/ parameters permit identification of dominant scatterers with a significant symmetric scattering component. The polarization orientation angle of these quasi-symmetric scatterers is used to derive an estimate of the ship's pitch angle, under certain conditions. The effect of SAR system focus setting errors and Doppler centroid mistracking on the SSCM performance is investigated. It is shown that the SSCM is sensitive to the system focus setting and Doppler centroid shift. The first-order effects of these errors can be removed prior to the application of the SSCM method.

The 17 papers in this special issue focus on the calibration and validation of ALOS sensors and their use for bio- and geophysical parameter retrievals.

The capability of multifrequency polarimetric synthetic aperture radar (SAR) to discriminate among nine vegetation classes is shown using both experimental data and model simulations. The experimental data were collected by the multifrequency polarimetric AIRSAR at the Dutch Flevoland site and the Italian Montespertoli site. Simulations are carried out using an electromagnetic model, developed at Tor Vergata University, Rome, Italy, which computes microwave vegetation scattering. The classes have been defined on the basis of geometrical differences among vegetation species, leading to different polarimetric signatures. It is demonstrated that, for each class, there are some combinations of frequencies and polarizations producing a significant separability. On the basis of this background, a simple, hierarchical parallelepiped algorithm is proposed.

The retrieval of soil surface parameters using a dual-polarization synthetic aperture radar operating in a compact polarimetric mode is examined. It is shown that by employing a two-component model of surface polarimetric coherency, soil surface parameter retrieval is possible directly from measurements without the need for any additional assumptions. Model surface parameters inverted by using the full polarimetric coherency matrix may be recovered equally well from a compact polarimetric coherency matrix.

For the polarimetric synthetic aperture radar interfe-rometry (PolInSAR) processing, it is necessary to coregister all the images, including the coregistration of polarimetric SAR images and the coregistration of interferometric SAR images. Otherwise, the performance of the estimated optimal interferograms will be deteriorated. A generalized scattering vector (GSV) model is proposed to execute the PolInSAR optimal interferograms estimation. The generalized scattering vector is constructed by the Pauli scattering vectors of the processing pixel and the surrounding pixels. Even though there are coregistration errors, all the polarimetric information of the current processing pixel is entirely included in the generalized scattering vector. Therefore, the GSV-based method can automatically recover the optimal scattering mechanisms of the processing pixel with coregistration errors either in interferoe-metric channels or polarimetric channels. Theoretical analysis and processing results of simulated PolSARPro data and real PALSAR data validate the effectiveness and correctness of the proposed method.

The purpose of this study was to assess the characteristics of the polarimetric radar clutter collected over various types of vegetation, and to evaluate the potential utility of various measures for classifying vegetated terrain. It utilized airborne radar data collected in October 1988 of pure and mixed stands of jack pine and aspen located in central Minnesota, and data collected in August 1989 of forested areas surrounding Durham, NC. The measured parameters are the polarimetric signature, a statistical distribution shape parameter, and the texture autocorrelation function. A method is presented for calculating the texture autocorrelation from single-look complex SAR imagery. Comparison of parameters from different clutter types at a variety of frequency, polarization, and incidence angle combinations showed that different terrain types exhibit unique polarimetric, statistical, and spatial properties. >