Polarimetric SAR Observations Research Articles

Potential of model-based polarimetric decomposition extended with multi-frequency and multi-incidence PolSAR observations

ABSTRACT This letter attempts to extend the model-based polarimetric decomposition (PD) theorems with multiple polarimetric synthetic aperture radar (PolSAR) observations. To consider both the surface and dihedral scattering depolarization effects, the X-Bragg model, the extended double Fresnel scattering model, and the step-wise volume scattering model are introduced in the model-based PD. The proposed methodology is explored by exploiting Airborne Synthetic Aperture Radar (AIRSAR) C-band and L-band data from southern Oklahoma, United States of America, and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric data from Winnipeg, Canada, with two flight lines of 31605-03 and 31606-03 in different incidence angles. The potential and superiority of the proposed decomposition method are examined by illustrating the false color composition images, and the power ratio statistics of surface, dihedral, and volume scattering components over selected bare soil, forest, grass, and urban patches.

Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass

ABSTRACT The penetration capability of electromagnetic wave signal into forest increases with increasing wavelength. SAR data at each frequency senses different components of forest structure. Therefore the biomass distributed at various tree components could be estimated using different radar frequencies. Additionally, the synthesis of multiple SAR frequencies could improve the accuracy in retrieving forest above-ground biomass (AGB). Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. We found that: (i) Most of the polarimetric observations are sensitive to forest AGB, (ii) GA-SVR performed well in forest AGB retrieval using the single frequency SAR observations or combinations of multi-frequency observations; the highest Acc. value for single-frequency-retrieved results is 75.13% acquired at P-band, with multi-frequency, the highest Acc. values is 77.34% acquired by combining C- and P-band. (iii) For forest AGB retrievals, the single-frequency P-band accuracy is comparable to the combined C- and P-band retrieval accuracy, indicating that the long-wavelength single-frequency P-band is sufficient for an accurate forest AGB retrieval. The findings reconfirmed potential of P-band for forest AGB retrievals, they also demonstrated that the optimal combination of multi-frequency SAR for AGB retrievals is by using a short-wavelength (X/C-) and a long-wavelength (L/P-).

Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified polarimetric scattering modelling, and limited SAR measurements. This study proposes a modified polarimetric decomposition framework to retrieve SM from multi-incidence and multitemporal PolSAR observations. The framework is constructed by combining the X-Bragg model, the extended double Fresnel scattering model and the generalised volume scattering model (GVSM). Compared with traditional decomposition models, the proposed framework considers the depolarisation of dihedral scattering and the diverse vegetation contribution. Under the assumption that SM is invariant for the PolSAR observations at two different incidence angles and that vegetation scattering does not change between two consecutive measurements, analytical parameter solutions, including the dielectric constant of soil and crop stem, can be obtained by solving multivariable nonlinear equations. The proposed framework is applied to the time series of L-band uninhabited aerial vehicle synthetic aperture radar data acquired during the Soil Moisture Active Passive Validation Experiment in 2012. In this study, we assess retrieval performance by comparing the inversion results with in-situ measurements over bean, canola, corn, soybean, wheat and winter wheat areas and comparing the different performance of SM retrieval between the GVSM and Yamaguchi volume scattering models. Given that SM estimation is inherently influenced by crop phenology and empirical parameters which are introduced in the scattering models, we also investigate the influence of surface depolarisation angle and co-pol phase difference on SM estimation. Results show that the proposed retrieval framework provides an inversion accuracy of RMSE<6.0% and a correlation of R≥0.6 with an inversion rate larger than 90%. Over wheat and winter wheat fields, a correlation of 0.8 between SM estimates and measurements is observed when the surface scattering is dominant. Specifically, stem permittivity, which is retrieved synchronously with SM also shows a linear relationship with crop biomass and plant water content over bean, corn, soybean and wheat fields. We also find that a priori knowledge of surface depolarisation angle, co-pol phase difference and adaptive volume scattering could help to improve the performance of the proposed SM retrieval framework. However, the GVSM model is still not fully adaptive because the co-pol power ratio of volume scattering is potentially influenced by ground scattering.

Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data

Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%.

Soil and Vegetation Scattering Contributions in L-Band and P-Band Polarimetric SAR Observations

Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vegetation parameter retrievals. In order to realize this constraint, an understanding of microwaves interaction with the surface and vegetation across frequencies is necessary. Different microwave frequencies have varied interactions with the soil–vegetation medium and increasing penetration into the soil and canopy with the decreasing frequency. In this study, we examine the contributions of different scattering mechanisms to coincident observations from two microwave frequencies (L and P) of airborne synthetic aperture radar instruments. We quantify contributions of surface, vegetation volume, and double-bounce scattering components. Results are analyzed and discussed to guide future multi-frequency retrieval algorithm designs.

Physically Based Polarimetric Volumetric Scattering From Cylindrically Dominated Vegetation Canopies

To advance the utility of polarimetric synthetic aperture radar observations for extracting biophysical information about vegetation canopies, it is important to develop good understanding of the scattering mechanisms that give rise to the polarimetric scattering response and to apply it toward the development of effective decomposition models of the polarimetric covariance matrices. In this paper, we introduce a more rigorous approach to characterizing the volume scattering component of the three-component scattering model developed by Freeman and Durden. The improved rigor has two aspects: 1) the use of a T-matrix model for computing the polarimetric scattering by an inclined dielectric cylinder and 2) the incorporation of the change in local incidence angle as a function of the orientation of the vegetation cylinders. The improvements address some of the current limitations of the Freeman–Durden model. This paper also provides a sensitivity analysis of the various components of the covariance matrix as a function of several physical parameters including cylinder size, its dielectric constant, and the orientation distribution of cylinders. Such an analysis is a precursor to the development of improved inversion algorithms.

Comparative Analysis of Polarimetric SAR Calibration Methods

In the diverse applications of polarimetric Synthetic Aperture Radar (SAR) systems, it is a crucial to conduct polarimetric calibration, which aims to remove the radar system distortion effects prior to utilizing polarimetric SAR observations. The objective of this study is to evaluate the performance of different polarimetric calibration methods. Two widely used methods, the Van Zyl and Quegan methods, and one recently proposed method, such as the Villa method, have been selected among various calibration methods in literature. The selected methods have basic differences in their assumptions that are applied to the polarimetric system model. In order to evaluate the calibration performances under different system parameters and ground characteristics, comparative analysis of the calibration results were conducted on synthetic polarimetric SAR data and ALOS PALSAR quad-pol mode data. Based on the experimental results, the advantages and limitations of different methods were clarified, and a simple hybrid calibration method is presented to further improve the polarimetric calibration performance.

ALOS-2 polarimetric SAR observation of Hokkaido-Iburi-Tobu earthquake 2018

ALOS-2 polarimetric SAR observation of Hokkaido-Iburi-Tobu earthquake 2018

Remote Predictive Mapping of the Tunnunik Impact Structure in the Canadian Arctic using Multispectral and Polarimetric SAR Data Fusion

The 28-km diameter Tunnunik impact structure in northern Victoria Island, Arctic Canada, was mapped using ASTER, Landsat 8, RADARSAT-2 polarimetric synthetic aperture radar (SAR), and Quickbird data. Multispectral analysis was accomplished through band ratios, MNF transform, and spectral matching algorithms, from which 4 distinct spectral units were defined. Polarimetric SAR decompositions also showed different scattering mechanisms for these 4 units indicating different surface roughness properties. These multispectral and polarimetric SAR observations were combined with detailed surface textures and morphological features as visible in very high-resolution Quickbird imagery (61 cm/pixel). Remote sensing parameters and their thresholds for characterizing each unit were implemented into a decision-tree algorithm and a remote predictive geological map was produced. Subsequent field and follow-up laboratory investigations enabled the ground-truthing of these predictions. The geological units were defined as follows: (i) (smooth) fluvioglacial deposits, (ii) (moderately rough) chert-bearing dolostone, (iii) (rough) dolostone, and (iv) (rough) dolostone covered by silicified surfaces. The rough surfaces characterized by multiple scattering in the polarimetric SAR decomposition correspond to the occurrences of weathered carbonate rocks, which are relatively resistant to weathering and form blocky surfaces. This shows that SAR-derived surface roughness properties can greatly contribute to defining geological units by combining with lithological mapping.

Effect of wind direction and incidence angle on polarimetric SAR observations of slicked and unslicked sea surfaces

The objective of this paper is to investigate the dependency of oil spill observations in polarimetric SAR data on imaging geometry, i.e., on incidence angle and look direction relative to the wind. The study is based on quad-polarization data acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar over experimental oil slicks under relatively high winds of 10–12 m/s over an 8-hour period. The data is collected over a wide range of incidence angles and alternates between looking upwind (UW) and downwind (DW). The unique time series enables a detailed study of the behavior of multipolarization parameters over clean sea and oil slicks under varying imaging geometry to be carried out for the first time. For clean sea backscatter, our findings are in agreement with previous studies, showing decreasing backscatter as the incidence angle increases and from UW to DW, with the highest sensitivity in the HH channel. We also find similar variations in oil covered areas. The results suggest that the oil slick backscatter is slightly more sensitive to the relative wind direction than the clean sea, and higher oil-sea damping ratios are found in DW than in UW cases, particularly in the HH channel. All multipolarization features investigated have some degree of dependency on imaging geometry. The lowest sensitivities are found in the magnitude of the copolarization correlation coefficient, the standard deviation of the copolarized phase difference, the polarization difference, the mean scattering angle and the entropy. Several features clearly change behavior when the signal approaches the sensor noise floor, and we find that the measurements and derived parameters may be affected at even higher signal-to-noise ratio (SNR) levels than previously proposed, i.e., closer to 7–9 dB above the sensor noise floor. Overall, the polarization difference is clearly identified as the most interesting parameter for oil spill observation, producing high oil-sea contrast in addition to low sensitivity to imaging geometry. The results show that both the relative wind direction and the incidence angle, in combination with the SNR, should be taken into account when developing operational methods based on multipolarization SAR data.

On the Spatial and Temporal Variability of L-band Polarimetric SAR Observations of Permafrost Environment in Central Yakutia

On the Spatial and Temporal Variability of L-band Polarimetric SAR Observations of Permafrost Environment in Central Yakutia

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

Application of Mixture Regression for Improved Polarimetric SAR Speckle Filtering

Speckle filtering is an indispensable operation in synthetic aperture radar (SAR) image processing but one which inevitably reduces image resolution. In order to preserve the intrinsic target features, adaptive speckle filters have been developed using weighted averages commensurate with the similarity of the target statistics. The target statistics are commonly derived from a prefiltering step which suffers from residue speckle contamination and feature smearing. In this paper, we adopted finite mixture models to characterize the observed in-scene variation and proposed a rigorous and progressive mixture regression method to better estimate the target statistics. The mixture model once fitted is able to capture the statistical properties of the highly textured and heterogeneous target variation, which is often observed in high-resolution SAR images. A nonlocal mean method is used for robust similarity evaluation of the local variation patterns between small image patches. The goal is to develop an improved polarimetric SAR (PolSAR) speckle filter that can accomplish a solid balance between speckle suppression and feature preservation. With the proposed filter, distinct scattering mechanisms and small-scale target features are retained from the start, even with single-look complex PolSAR observations. We test the algorithm using simulated data and single-look high-resolution PolSAR images: one acquired by DLR's F-SAR system and one by DLR's E-SAR system.

Application of Freeman decomposition to full polarimetric GPR for improving subsurface target classification

Migration technique can reconstruct the subsurface target imaging from record ground penetrating radar (GPR) data. Depending on the reconstructed imaging, we possibly distinguish subsurface target by the geometrical feature. But some different targets have similar reconstructed imaging, which will confuse us. Freeman decomposition is a technique for fitting a three-component scattering mechanism model to polarimetric synthetic aperture radar observations, which is successfully used in the classification of terrain objects. This paper applies the model-based Freeman decomposition to full polarimetric GPR data to improve the subsurface target classification. We use a full polarimetric GPR, which is constructed in laboratory with three types of antenna combinations, HH, VV and VH combinations, to acquire experiment data sets for testing the method. Metallic plate, ball, dihedral and volume scatter with many branches, are buried in the homogenous dry sand under flat ground surface, and three dimensional data sets are acquired above each buried target. After signal processing, we obtained subsurface target imaging by migration and color-coded polarimetric information by Freeman decomposition. Results showed that it can improve the classification capability of GPR for the subsurface target to use both the geometrical feature and polarimetric information.

The Effect of Topography on Target Decomposition of Polarimetric SAR Data

Polarimetric target decomposition enables the interpretation of radar images more easily, mostly based on physical assumptions, i.e., fitting physically-based scattering models to the polarimetric SAR observations. However, the model-fitting result cannot be always successful. Particularly, the performance of model-fitting in sloping forests is still an open question. In this study, the effect of ground topography on the model-fitting-based polarimetric decomposition techniques is investigated. The estimation accuracy of each scattering component in the decomposition results are evaluated based on the simulated target matrix by using the incoherent vegetation scattering model that accounts for the tilted scattering surface beneath the forest canopy. Experimental results show that the surface and the double-bounce scattering components can be significantly misestimated due to the topographic slope, even when the volume scattering power is successfully estimated.

Coastal wind field retrieval from polarimetric synthetic aperture radar

Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar (SAR) with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models (NWP), which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex (SLC) data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured. This article is a useful addition to the work of independent SAR wind retrieval. The experimental results herein show that it is feasible to employ the co-polarimetric backscattering coefficients and the polarimetric correlation backscattering coefficients for coastal wind field retrieval.

Synergetic use of SAR and Thermal Infrared data to study the physical properties of the lunar surface

The surface layer of the Moon preserves vital evidences of lunar impact and cratering processes due to the absence of any Aeolian and fluvial erosion processes acting on it. By examining these evidences, which are recorded throughout the evolutionary history of the Moon, several basic aspects of lunar science can be understood, and this has direct relevance to the surfaces of other airless bodies within the solar system. In this study, rock abundance data obtained from Thermal Infrared (TIR) observations and radar Circular Polarization Ratio (CPR) data sets obtained from polarimetric SAR observations were correlated at some sample sites on the lunar surface. Preliminary results yielded qualitative and quantitative estimates for surface rock abundances. Except at distal ejecta deposits of young, bright craters a general correlation was observed between the two datasets. Mixed results were observed from the impact melt flows where the situation is complex due to the possible subsurface-volume and volume-subsurface interactions of the radar waves. But the flow features were clearly separated from the interior and ejecta regions of their parent craters in terms of CPR and rock abundances. The extent and distributions of pyroclastic deposits and dark haloed regions could not be distinctly identified at the resolution of datasets utilized. Near Gerasimovich D crater, the Diviner Radiometer has provided the first TIR observations of a newly discovered impact melt flow which was not visible in the optical imagery. This facilitated the first ever quantitative comparisons of the radar CPR and rock abundance values near such a region. Also, significant differences in spatial patterns between the radar and rock concentration data sets were observed, owing to the differences in the sensitivity of the two observations.

Three-Dimensional Imaging and Scattering Mechanism Estimation Over Urban Scenes Using Dual-Baseline Polarimetric InSAR Observations at L-Band

This paper introduces new polarimetric algorithms for generating 3-D images and estimating scattering mechanisms from polarimetric multibaseline (MB) interferometric synthetic aperture radar (SAR) measurements. First, an MB interferometric SAR signal model is generalized to the fully polarimetric configuration, establishing the notion of polarimetric reflectivity. Subsequently, polarimetric beamforming, Capon, and MUSIC methods that determine optimal polarization combinations for height estimation are developed. These new techniques allow for extracting the height of reflectors, the associated scattering mechanisms, and the polarimetric (pseudo)reflectivities. By means of polarimetric dual-baseline interferometric SAR observations of an urban environment, the performance of the conceived algorithms is examined in detail. Producing 3-D images of a building layover, the quality of the approaches is compared in terms of refined resolution and lowered side lobes. Furthermore, the scattering processes occurring in urban scenes are investigated thoroughly by analyzing the optimal reflection types. The algorithms are validated using dual-baseline polarimetric SAR interferometric data at L-band acquired by German Aerospace Center's experimental SAR system over Dresden city.

Crop classification in rain-fed and irrigated agricultural areas using Landsat TM and ALOS/PALSAR data

Multispectral optical remote-sensing systems have been the base for crop identification and monitoring for many years. However, cloud cover limits and even prevents the use of optical data for this activity. Synthetic aperture radar (SAR) sensors provide an interesting alternative to conventional multispectral classification schemes. Radar sensors can acquire data regardless of cloud cover, and their observations provide complementary information to that captured by optical sensors. The main objective of this paper is to evaluate whether the incorporation of polarimetric SAR observations to a multispectral classification scheme enhances classification results using the proposed method. With this aim, one Landsat thematic mapper scene and two ALOS/PALSAR quadrature polarization scenes acquired in 2007 were processed. The results demonstrated the ability of SAR data to improve the classification based on optical images. However, improvements were slight (an increase of around 2% in the classification's overall accuracy). The results improved significantly when barley, wheat, and oats were considered a single class, called cereals. The best results were achieved using Landsat and the two ALOS/PALSAR scenes together, obtaining an overall Kappa coefficient and accuracy of 0.67 and 90%, respectively. Probably, scenes acquired on other dates (June and July) would have yielded clearer classification enhancements.

Estimation of Surface Roughness Parameter in Intertidal Mudflat Using Airborne Polarimetric SAR Data

The coastal zones of the Korean peninsula are well known for their large tide ranges and vast expanse of intertidal flats. In this paper, methods of extracting the roughness of the scattering surface of intertidal mudflats from polarimetric synthetic aperture radar (SAR) data have been investigated. The L-band NASA/Jet Propulsion Laboratories airborne SAR data, which were acquired in the intertidal zone during PACRIM-II Korea campaign, were used to estimate the roughness of intertidal mudflats. Surface roughness can be utilized as a useful parameter to monitor the fishery activities in intertidal flats as well as the changes in textural characteristics of surface sediments. In order to retrieve roughness parameters, such as the rms height and the correlation length, of intertidal mudflats, three types of roughness inversion algorithms, based on the Integral Equation Method (IEM), semiempirical, and extended-Bragg models, have been investigated and developed. The inversion algorithms based on the IEM and semiempirical models can be applied to the dual-polarized SAR, while the extended-Bragg model-based inversion approach is also applicable to the fully polarimetric SAR observations. Results indicate the fully polarimetric approach is more pertinent to monitor geophysical parameters from space than the dual polarimetric approach, even if it is possible to reduce the number of unknown surface variables in the specific case of inversion problems.