Polarimetric Imagery Research Articles

A Review on PolSAR Decompositions for Feature Extraction.

Feature extraction plays a pivotal role in processing remote sensing datasets, especially in the realm of fully polarimetric data. This review investigates a variety of polarimetric decomposition techniques aimed at extracting comprehensive information from polarimetric imagery. These techniques are categorized as coherent and non-coherent methods, depending on their assumptions about the distribution of information among polarimetric cells. The review explores well-established and innovative approaches in polarimetric decomposition within both categories. It begins with a thorough examination of the foundational Pauli decomposition, a key algorithm in this field. Within the coherent category, the Cameron target decomposition is extensively explored, shedding light on its underlying principles. Transitioning to the non-coherent domain, the review investigates the Freeman-Durden decomposition and its extension, the Yamaguchi's approach. Additionally, the widely recognized eigenvector-eigenvalue decomposition introduced by Cloude and Pottier is scrutinized. Furthermore, each method undergoes experimental testing on the benchmark dataset of the broader Vancouver area, offering a robust analysis of their efficacy. The primary objective of this review is to systematically present well-established polarimetric decomposition algorithms, elucidating the underlying mathematical foundations of each. The aim is to facilitate a profound understanding of these approaches, coupled with insights into potential combinations for diverse applications.

Method for Remote Sensing Oil Spill Applications Over Thermal and Polarimetric Imagery

Method for Remote Sensing Oil Spill Applications Over Thermal and Polarimetric Imagery

Sparse representation-based demosaicking method for joint chromatic and polarimetric imagery

Division of focal plane (DoFP) polarization camera has become mainstream among various polarimetric imaging systems whose sensor is equipped with Bayer filter and directional polarization filter, so joint chromatic and polarimetric image demosaicking is essential. However, since 15 or 14 pixels are missed out of 16 pixels in color polarization mosaic images, it’s challenging to adopt a method to reconstruct full-resolution images well. In this paper, we propose a demosaicking model based on sparse coding. The model introduces RGB-polarization channels correlation, adaptive sub-dictionaries and non-local self-similarity restrictions, and combines them into an optimal problem to solve. The experimental results, including reconstructing full-resolution synthetic images and camera captured real scenes, demonstrate that our proposed method outperforms the current state-of-the-art method in terms of quantitative measures and visual quality.

Potential of ALOS2 Polarimetric Imagery to Support Management of Poplar Plantations in Northern Italy

Poplar is one of the most widespread fast-growing forest species. In Northern Italy, plantations are characterized by large interannual fluctuations, requiring frequent monitoring to inform on wood supply and to manage the stands. The use of radar satellite data is proving useful for forest monitoring, being weather independent and sensitive to the changes in forest canopy structure, but it has been scarcely tested in the case of poplar. Here, L-band ALOS2 (Advanced Land Observing Satellite-2) dual-pol data were tested to detect clear-cut plantations in consecutive years. ALOS2 quad-pol data were used to discriminate among different age classes, a much complex task than detecting poplar plantations extent. Results from different machine learning algorithms indicate that with dual-pol data, poplar forest can be discriminated from clear-cut areas with 80% overall accuracy, similar to what is usually obtained with optical data. With quad-pol data, four age classes were classified with moderate overall accuracy (73%) based on polarimetric decompositions, three 3 age classes with higher accuracy (87%) based on HV band. Sources of error are represented by poplar areas of intermediate age when stems, branches and leaves were not developed enough to detect by scattering mechanisms. This study demonstrates the feasibility of monitoring poplar plantations with satellite radar, which represents a growing source of information thanks to already-planned future satellite missions.

Mixed noise removal based on Stokes residual noise removal for division of focal plane polarimetric images

The division of focal plane (DoFP) polarimeter has the advantages of simultaneous imaging and compact optical structure. It is inevitable to introducing the noise in the process of micro-polarizer array integration, polarization image acquisition and transmission. In this paper, we propose a novel mixed noise suppression method based on Stokes residual noise removal (SRNR) for mixed additive white Gaussian noise (AWGN) and impulse noise (IN) removal in DoFP polarimetric images. The Laplacian scale mixture (LSM) model is introduced to estimate the IN and the nonlocal low-rank regularization (NLR) is adopted to enhance the denoising performance. The residual noise of the Stokes parameters is taken into consideration. This crucial processing achieves the acquisition of high-quality polarization data. The experiments demonstrate the superior denoising performance for polarimetric imagery in terms of objective assessment and visual evaluation.

Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery

Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a “Wishart mixture model” (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches, and that the GC algorithm provides the best optimization.

Framework for Reconstruction of Pseudo Quad Polarimetric Imagery from General Compact Polarimetry

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.

NESZ Estimation and Calibration for Gaofen-3 Polarimetric Products by the Minimum Noise Envelope Estimator

The Chinese Gaofen-3 satellite currently provides us with an open way to access fully polarimetric data in the C-band frequency. The noise equivalent sigma zero (NESZ) is a crucial factor when calibrating the additive noise in radar imagery. For most radar sensors, NESZ coefficients are stored in header files, but these are not provided for the Gaofen-3 products. The minimum eigenvalue estimator (MEE) and maximum likelihood estimator (MLE) are the two most common techniques used to derive the NESZ from polarimetric imagery. Nevertheless, the bias has been found to be higher than 5 dB compared with the noise measurement circuit (NMC) of the hardware. In this article, we propose a minimum noise envelope estimator (MNEE) for the robust estimation of the Gaofen-3 NESZ. In this article, we carried out an in-depth investigation to analyze the error sources of the MEE and MLE techniques. Based on our analysis, the MNEE framework requires the use of the ocean surface as a reference, and MNEE is combined with the minimum operation to suppress overestimation. In the experimental section, we describe how we validated the proposed algorithm with Radarsat-2 images, and the MNEE is treated as a tool to estimate the NESZ of Gaofen-3 polarimetric products. We found that the Gaofen-3 NESZ is generally less than -20 dB, which satisfies the design specification. The range-dependent NESZ coefficients are provided here to allow convenient noise correction for Gaofen-3 data users.

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

Multispectral Polarimetric Imagery (MSPI) contains significant information about an object’s distribution, shape, shading, texture and roughness features which can distinguish between foreground and background in a complex scene. Due to spectral signatures being limited to material properties, Background Segmentation (BS) is a difficult task when there are shadows, illumination and clutter in a scene. In this work, we propose a two-fold BS approach: multiband image fusion and polarimetric BS. Firstly, considering that the background in a scene is polarized by nature, the spectral reflectance and correlations and the textural features of MSPI are calculated and analyzed to demonstrate the fusion significance. After that, integrating Principal Component Analysis (PCA) with Fast Fourier Transform (FFT), a hybrid fusion technique is proposed to show the multiband fusion effectiveness. Secondly, utilizing the Stokes vector, polarimetric components are calculated to separate a complex scene’s background from its foreground by constructing four significant foreground masks. An intensity-invariant mask is built by differentiating between the median filtering versions of unpolarized and polarized images. A strongly unpolarized foreground mask is also constructed in two different ways, through analyzing the Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). Moreover, a strongly polarized mask and a strong light intensity mask are also calculated based on the azimuth angle and the total light intensity. Finally, all these masks are combined, and a morphological operation is applied to segment the final background area of a scene. The proposed two-fold BS algorithm is evaluated using distinct statistical measurements and compared with well-known fusion methods and BS methods highlighted in this paper. The experimental results demonstrate that the proposed hybrid fusion method significantly improves multiband fusion quality. Furthermore, the proposed polarimetric BS approach also improves the mean accuracy, geometric mean and F1-score to 0.95, 0.93 and 0.97, respectively, for scenes in the MSPI dataset compared with those obtained from the methods in the literature considered in this paper. Future work will investigate mixed polarized and unpolarized BS in the MSPI dataset with specular reflection.

Metal target detection method using passive millimeter-wave polarimetric imagery.

Polarization-based passive millimeter-wave imaging has been applied in several applications, including material clustering, pattern recognition, and target detection. We present here a general formulation of a metal target detection method called dual linear polarization discriminator (DLPD), utilizing passive millimeter-wave polarimetric imagery. Several potential discriminators are defined, and linear polarization difference ratio (LPDR) is selected and proposed to be a new feature discriminator that is sensitive to material composition and able to reduce ambient radiation effects when detecting target with different material and shape. Furthermore, the detection criterion is verified utilizing the threshold values determined by a statistical analysis of LPDR. Outdoor experiments demonstrate that the proposed detection method is highly effective for detecting a metal target in a complex background.

Convolutional demosaicing network for joint chromatic and polarimetric imagery.

Due to the latest progress in image sensor manufacturing technology, the emergence of a sensor equipped with an RGGB Bayer filter and a directional polarizing filter has brought significant advantages to computer vision tasks where RGB and polarization information is required. In this regard, joint chromatic and polarimetric image demosaicing is indispensable. However, as a new type of array pattern, there is no dedicated method for this challenging task. In this Letter, we collect, to the best of our knowledge, the first chromatic-polarization dataset and propose a chromatic-polarization demosaicing network (CPDNet) to address this joint chromatic and polarimetric image demosaicing issue. The proposed CPDNet is composed of the residual block and the multi-task structure with the costumed loss function. The experimental results show that our proposed methods are capable of faithfully recovering full 12-channel chromatic and polarimetric information for each pixel from a single mosaic image in terms of quantitative measures and visual quality.

Classification of oil spill by thicknesses using multiple remote sensors

Satellite Synthetic Aperture Radar (SAR) is an operational tool for monitoring and assessment of oil spills. Satellite SAR has primarily been used to detect the presence/absence of oil, yet its ability to discriminate oil emulsions within a detected oil slick has not been fully exploited. Additionally, one of the challenges in the past has been the ability to deliver strategic information derived from satellite remote sensing in a timely fashion to responders in the field. This study presents methods for the rapid classification of oil types and estimated thicknesses, from which information about thick oil and oil emulsions (i.e., “actionable” oil) can be delivered in an operational timeframe to responders in the field. Experiments carried out at the OHMSETT test facility in New Jersey demonstrate that under specific viewing conditions, a single polarization satellite SAR image can record a signal variance between thick stable emulsions and non-emulsified oil. During a series of field campaigns in the Gulf of Mexico with in situ measurements of oil thickness, multiple satellite data were obtained including fully polarimetric C-band SAR imagery from RADARSAT-2 and multispectral imagery from ASTER and WorldView-2. One campaign included the airborne polarimetric UAVSAR L-band sensor. An oil/emulsion thickness classification product was generated based on RADARSAT-2 polarimetric imagery using entropy and the damping ratio derivations. Herein, we present the classification methods to generate oil thickness products from SAR, validated by sea-truth observations, the multispectral imagery, and the UAVSAR data. We tested the ability to deliver these products with minimum latency to responding vessels via NOAA. During field operations in the Gulf of Mexico, a satellite SAR-based product of oil delineation by relative thickness was delivered to a responding vessel 42 min after the RADARSAT-2 data acquisition. This proof-of-concept test using satellite SAR and multispectral imagery to detect emulsions and deliver a derived information product to a vessel in near-real-time points directly to methods for satellite-based assets to be used in the near future for oil spill tactical response operations.

Constant false alarm rate detection method in polarimetric synthetic aperture radar imagery based on whitening filter under inverse beta distribution

The constant false alarm rate (CFAR) detection method based on multi-look polarisation whitening filter (MPWF) has not been obtained in the polarimetric synthetic aperture radar imagery when the texture variable obeys the Inverse Beta distribution. To solve the problem, a new CFAR detection method with analytical expression for false alarm rate is proposed. First, the probability density function (PDF) of the output via MPWF is derived based on the hypothesis of the texture variables obeying the Inverse Beta distribution in the product model. Secondly, the analytical expression of false alarm rate about detection threshold is obtained by integrating the PDF of MPWF output, and the procedure of the CFAR detection method is designed. Finally, the performance comparisons are made among the new method, Wishart CFAR detection algorithm and the two-parameter CFAR method via simulated and real data of AIRSAR. The results show that figure of merit of the new method is the highest and the new method gives the best performance in Inverse Beta distribution case.

Multi-epoch Direct Imaging and Time-variable Scattered Light Morphology of the HD 163296 Protoplanetary Disk

We present H-band polarized scattered light imagery and JHK high-contrast spectroscopy of the protoplanetary disk around HD 163296 observed with the HiCIAO and SCExAO/CHARIS instruments at Subaru Observatory. The polarimetric imagery resolve a broken ring structure surrounding HD 163296 that peaks at a distance along the major axis of 0.65 (66 AU) and extends out to 0.98 (100 AU) along the major axis. Our 2011 H-band data exhibit clear axisymmetry, with the NW- and SE- side of the disk exhibiting similar intensities. Our data are clearly different than 2016 epoch H-band observations from VLT/SPHERE that found a strong 2.7x asymmetry between the NW- and SE-side of the disk. Collectively, these results indicate the presence of time variable, non-azimuthally symmetric illumination of the outer disk. Based on our 3D-MCRT modeling of contemporaneous IR spectroscopic and H-band polarized intensity imagery of the system, we suggest that while the system could plausibly host an inclined inner disk component, such a component is unlikely to be responsible for producing the observed time-dependent azimuthal variations in the outer scattered light disk of the system. While our SCExAO/CHARIS data are sensitive enough to recover the planet candidate identified from NIRC2 in the thermal IR, we fail to detect an object with a corresponding JHK brightness estimated from the atmospheric models of Baraffe et al. 2003. This suggests that the candidate is either fainter in JHK bands than model predictions, possibly due to extinction from the disk or atmospheric dust/clouds, or that it is an artifact of the dataset/data processing. Our SCExAO/CHARIS data lower the IR mass limits for planets inferred at larger stellocentric separations; however, these ALMA-predicted protoplanet candidates are currently still consistent with direct imaging constraints.

A Three-Hierarchy Evaluation of Polarimetric Performance of GF-3, Compared with ALOS-2/PALSAR-2 and RADARSAT-2.

GaoFen-3 (GF-3) is the first Chinese civilian multi-polarization synthetic aperture radar (SAR) satellite, launched on 10 August of 2016, and put into operation at the end of January 2017. The polarimetric SAR (PolSAR) system of GF-3 is able to provide quad-polarization (quad-pol) images in a variety of geophysical research and applications. However, this ability increases the complexity of maintaining image quality and calibration. As a result, to evaluate the quality of polarimetric data, polarimetric signatures are necessary to guarantee accuracy. Compared with some other operational space-borne PolSAR systems, such as ALOS-2/PALSAR-2 (ALOS-2) and RADARSAT-2, GF-3 has less reported calibration and image quality files, forcing users to validate the quality of polarimetric imagery of GF-3 before quantitative applications. In this study, without the validation data obtained from a calibration infrastructure, an innovative, three-hierarchy strategy was proposed to assess PolSAR data quality, in which the performance of GF-3 data was evaluated with ALOS-2 and RADARSAT-2 data as references. Experimental results suggested that: (1) PolSAR data of GF-3 satisfied backscatter reciprocity, similar with that of RADARSAT-2; (2) most of the GF-3 PolSAR images had no signs of polarimetric distortion affecting decomposition, and the system of GF-3 may have been improved around May 2017; and (3) the classification accuracy of GF-3 varied from 75.0% to 91.4% because of changing image-acquiring situations. In conclusion, the proposed three-hierarchy approach has the ability to evaluate polarimetric performance. It proved that the residual polarimetric distortion of calibrated GF-3 PolSAR data remained at an insignificant level, with reference to that of ALOS-2 and RADARSAT-2, and imposed no significant impact on the polarimetric decomposition components and classification accuracy.

Application of Full Polarimetric ALOS PALSAR for Land Cover Mapping In Sleman District

The simplest way to interpret polarimetric imagery for land cover classification is to use visualinterpretation methods. The existence of interpretations key as a tool for visual interpretation becomesimportant when different interpreters can produce different results. The quality of the results of theinterpretation of land cover is then determined by the quality of the interpretation tool, in this case, thekey to the interpretation of land cover. The purpose of this study was to make the key to land coverclass interpretation in the Full Polarimetric ALOS PALSAR image, then the interpretation key wasused for reference in making land cover maps and measuring the accuracy of the results of the visualinterpretation. The image used in this study consisted of HH, VV, HV and VH bands. The location ofthe study was in parts of Sleman District. The analysis is done visually by on-screen digitizing onALOS Palsar composite HH + VV HV + VH HH-HV image, which is then interpreted key. The truetest is done by means of the overall accuracy test and Kappa. Visually, ALOS PALSAR imagery isable to distinguish 12 land cover classes in the research area, namely built land, rice fields, mixedgardens, moorlands, salak garden, grass, forest, shrubs, open land, airports, water bodies and lavawith 83% Overall accuracy, and 78% Kappa accuracy.

Material Clustering Using Passive Millimeter-Wave Polarimetric Imagery

Passive millimeter-wave imaging has emerged as a useful tool in many remote sensing applications, including resource remote sensing, material classification, and target detection. This paper presents a method to classify specular objects based on their material composition from passive millimeter-wave polarimetric imagery. Passive degree of polarization (PDoP) is proposed and calculated by using the results of a passive millimeter-wave polarization measurement. The PDoP values of typical ground targets are further analyzed. Outdoor experiments are conducted, and the PDoP image is generated from the brightness temperature images. The PDoP values of pixels in the image are statistically analyzed, and the threshold values are set based on the statistical analysis results, then every pixel is recognized. Experimental results indicate that this method is highly effective for distinguishing among various materials of interest.

Quad and compact multitemporal C-band PolSAR observations for crop characterization and monitoring

Monitoring crop condition using optical satellite indices has a legacy of several decades. Early warning of variances in crop production is vital in mitigating regional and global food insecurity. Adoption of optical vegetation indices for this purpose is widespread, yet cloud cover impedes the acquisition of these data. Although early research using scatterometers and aircraft hinted at the sensitivity of Synthetic Aperture Radar (SAR) responses to crop development, the implementation of satellite SAR observations in operational crop condition monitoring is limited. In the research presented here, volume-to-surface (V/S) scattering ratios derived from C-band RADARSAT-2 quad and simulated compact polarimetric (QP and CP) imagery are assessed for their potential to monitor crop growth. Both V/S ratios were strongly correlated with optical vegetation indices, including the widely adopted Normalized Difference and Soil Adjusted Vegetation Indices. The changes in the ratio of volume to surface scattering were correlated with variations in crop biomass. The results support the potential of a SAR scattering ratio for crop condition monitoring. In particular, encouraging results were reported for compact polarimetry, a mode that can be implemented to deliver broader swath coverage conducive to regional and national monitoring.

Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic

Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user’s and producer’s accuracies were 5–24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71–77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89–91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic.

Computational polarimetric microwave imaging

We propose a polarimetric microwave imaging technique that exploits recent advances in computational imaging. We utilize a frequency-diverse cavity-backed metasurface, allowing us to demonstrate high-resolution polarimetric imaging using a single transceiver and frequency sweep over the operational microwave bandwidth. The frequency-diverse metasurface imager greatly simplifies the system architecture compared with active arrays and other conventional microwave imaging approaches. We further develop the theoretical framework for computational polarimetric imaging and validate the approach experimentally using a multi-modal leaky cavity. The scalar approximation for the interaction between the radiated waves and the target- often applied in microwave computational imaging schemes-is thus extended to retrieve the susceptibility tensors, and hence provides additional information about the targets. Computational polarimetry has relevance for existing systems in the field that extract polarimetric imagery, and particular for ground observation. A growing number of short-range microwave imaging applications can also notably benefit from computational polarimetry, particularly for imaging objects that are difficult to reconstruct when assuming scalar estimations.