Dual-polarization Synthetic Aperture Radar Data Research Articles

Ecosystem assessment in the Tonle Sap Lake region of Cambodia using RADARSAT-2 Wide Fine-mode SAR data

The Tonle Sap Lake (TSL), located in Cambodia, is the largest freshwater lake in Southeast Asia and has significant ecological, economic, and sociocultural value. The TSL’s ecosystems have been affected by climate change and an increasing amount of human activity in recent years. Considering that the TSL area is often covered by clouds, particularly in the rainy season, synthetic aperture radar (SAR) data are suitable for assessing the ecosystems in this great lake, as SAR enables weather- and cloud-independent observations. In this study, we investigated the capability of the RADARSAT-2 Wide Fine (WF) mode dual-polarization SAR data with a scene size of 170 × 150 km (azimuth × range) and a resolution of 7.6 × 5.2 m to study TSL’s ecosystem, by analysing the usefulness of backscattering coefficients and scattering mechanism-related parameters in identifying artificial targets and different land-cover types. The results of this study demonstrate the applicability of RADARSAT-2 WF-mode SAR data in the study of TSL’s ecosystems.

Reflection Symmetry for Polarimetric Observation of Man-Made Metallic Targets at Sea

In this paper, a new paradigm for coherent microwave polarimetric remote sensing observation of man-made metallic targets at sea is presented and demonstrated over real polarimetric synthetic aperture radar (SAR) data. It is based on the different symmetry properties of the sea and of the man-made targets. The rationale relies on physical principles instead of standard image processing. Hence, a simple and very effective filter to observe man-made metallic targets at sea by full-resolution dual-polarized SAR data is developed. The technique is compared to a classic polarimetric approach, namely the polarimetric cross-entropy (PCE) constant false alarm rate (CFAR), and it is shown to perform best. Experiments, undertaken over a large data set of single look complex (SLC) L-band ALOS-PALSAR and C-band RADARSAT-2 full-polarimetric SAR data, demonstrate the physical soundness of the theory and the effectiveness of the approach from an operational viewpoint.

Dual-Polarized TerraSAR-X Data for Oil-Spill Observation

A study exploiting dual-polarimetric X-band synthetic aperture radar (SAR) data to observe oil at sea is undertaken for the first time. The polarimetric model exploits the interchannel correlation between the like polarized channels. Accordingly, two parameters related to the interchannel correlation, namely, the amplitude coherence and the copolarized phase difference (CPD) standard deviation, are accounted for, and their performances, with respect to sea oil slick observation, are carefully discussed. Single-look Slant range Complex dual-polarized TerraSAR-X SAR data, in which both certified oil slicks and weak-damping look-alikes are present, are used to verify the efficiency of the proposed approaches. Results show the advantage of the CPD approach and the effectiveness of TerraSAR-X dual-polarized products for such application.

A multi-level segmentation methodology for dual-polarized SAR data

An innovative methodology for dual-polarized Synthetic Aperture Radar (SAR) data segmentation is proposed. The methodology is based on the thresholding of the 1D-histograms of the two images produced by the dual polarimetric bands. Thresholding of the histograms is performed using a nonparametric algorithm. Histograms after thresholding are combined together in a two dimensional histogram-based space in order to define sub-spaces, which are used for image segmentation. Sub-spaces are further divided based on two criteria which lead to a multi-level segmentation approach. Dual-polarized TerraSAR-X data, both HH and VV, are used in a study area located in the southwestern United Kingdom.

TerraSAR-X Data for Burn Severity Evaluation in Mediterranean Forests on Sloped Terrain

TerraSAR-X (TSX) dual-polarized synthetic aperture radar (SAR) data from a test site in Spain have been investigated to determine the relationship between forest burn severity and SAR backscatter. The role of the local incidence angle on the backscatter coefficient has been also studied. Burn severity was estimated by means of composition burn index plots and the remotely sensed differenced normalized burn ratio index. To infer the potential of the TSX data for burn severity assessment, the determination coefficients obtained from linear regression analysis have been used. At horizontal transmit horizontal receive (HH) polarization, backscatter increased for slopes oriented toward the sensor and areas affected by high burn severity, whereas, at horizontal transmit vertical receive (HV) polarization, higher backscatter occurred for slopes oriented away from the sensor in areas of low burn severity. The dependence of the backscatter coefficient on topography for areas affected by forest fire has been confirmed. The HH backscatter presented a clear descending trend with the increase in local incidence angle, whereas the HV backscatter presented an ascending trend. The determination coefficients showed that, at HH polarization, better estimates of burn severity are obtained at low local incidence angles, whereas, for HV polarization, the best estimates are obtained at high local incidence angles. The dual-polarized X-band SAR data showed potential for burn severity estimation in the Mediterranean environment if local incidence angle is accounted for.

Snowpack Characterization in Mountainous Regions Using C-Band SAR Data and a Meteorological Model

This paper presents a method to characterize snow cover in mountainous regions using dual-polarization C-band synthetic aperture radar (SAR) data. It is demonstrated that an accurate modeling of the liquid water distribution inside the snowpack, using a multilayer meteorological snow model, is required to characterize snow with precision. A multilayer-snow electromagnetic (EM) backscattering model is developed based on the vector radiative transfer, the strong fluctuation theory, and physical parameters supplied by the meteorological model. However, the limited resolution of the meteorological snow model is insufficient for predicting a refined EM backscattering at a massif scale. An adequate spatial reorganization of these snow profiles, based on a comparison between simulated and measured dual-polarization SAR data, leads to a better estimation of some snowpack parameters. In particular, the monitoring of snow liquid water content is presented improving the capacity of wet snow mapping as compared to a classical SAR-based method. This methodology shows good capacities both for qualitative and quantitative snow assessments, opening the way for a new operational method.

Sea-Ice thickness retrieval in the Sea of Okhotsk using dual-polarization SAR data

AbstractWe investigated the feasibility of using multi-polarization Synthetic aperture radar (SAR) data to estimate the thickness of undeformed first-year ice. Analysis of the radar Signatures for the C- and L-bands Showed that the correlation between the ice thickness and VV-to-HH backscattering ratio is larger than the correlation between the ice thicknesses and the backscattering coefficients. This is in part because the ice Surface Salinity and hence the Surface reflection coefficient decreases as the ice thickens. The backscattering ratio had low Sensitivity to the Small-scale ice Surface roughness for the C-band and is almost independent of roughness at L-band. Given that the ratio is most Sensitive to ice Surface dielectric constants, which depend on Salinity, we developed an algorithm for retrieving the ice thickness that is based on the backscattering ratio and on the integral-equation-method (IEM) Surface Scattering model. Comparison of the observed and estimated ice thicknesses Showed that the correlation was much better when the thicknesses were estimated using the backscattering ratio than when the backscattering coefficient was used directly. The algorithm also performed better than previous retrievals using an empirical technique.

Retrieval of ocean wave spectra and RAR MTF's from dual-polarization SAR data

A method for the retrieval of the real aperture radar (RAR) modulation transfer function (MTF) and ocean wave spectra from dual-polarization (i.e., simultaneously acquired HH and VV polarizations) synthetic aperture radar (SAR) image data is described. The RAR MTF is estimated by applying empirical MTF estimation methodologies to inter-look cross spectra between various combinations of individual looks and available polarizations for a given radar frequency. The concept behind the nonlinear inversion is that any combination of like- and cross-polarization image spectra should return the same wave spectrum, in agreement with in situ and model wave spectra. This permits estimation of the RAR MTF on a case-by-case basis. The results are compared with theoretical treatments of the RAR MTF, which are shown to be inadequate for the range of conditions encountered in their data set. However, the theory and measurements fit well in describing the polarization dependence of the RAR MTF. The data set consists of SIR-C/X-SAR L-band and CCRS CV-580 C-band SAR data, in situ buoy measurements, and model data from field programs in Canadian waters in October and December 1994.

Segmentation of polarimetric synthetic aperture radar data

A statistical image model is proposed for segmenting polarimetric synthetic aperture radar (SAR) data into regions of homogeneous and similar polarimetric backscatter characteristics. A model for the conditional distribution of the polarimetric complex data is combined with a Markov random field representation for the distribution of the region labels to obtain the posterior distribution. Optimal region labeling of the data is then defined as maximizing the posterior distribution of the region labels given the polarimetric SAR complex data (maximum a posteriori (MAP) estimate). Two procedures for selecting the characteristics of the regions are then discussed. Results using real multilook polarimetric SAR complex data are given to illustrate the potential of the two selection procedures and evaluate the performance of the MAP segmentation technique. It is also shown that dual polarization SAR data can yield segmentation resultS similar to those obtained with fully polarimetric SAR data.

Characterization of sea ice types using synthetic aperture radar

The results of an investigation into the use of synthetic aperture radar (SAR) imagery for sea ice-type discrimination are presented. X- and L-band dual-polarization SAR data of Beaufort Sea ice were examined using manual interpretation techniques to determine which channel provides the most information. Quantitative methods for ice-type discrimination also were explored by statistical parameterization of these data. Various statistical tests, both parametric and nonparametric, were applied to evaluate the utility of the parameters for machine interpretation of SAR ice data. The results obtained indicate that, under winter ice conditions, X-nband is superior to L-band for discriminating various ice types. Also, imagery obtained at small incidence angles shows greater tonal variation between ice types than that obtained at larger angles. Of the quantitative measures evaluated, mean and standard deviation appear to be the most valuable. Examination of quantities involving higher order moments indicates that a better understanding of the SAR imaging process is required before these measures can be utilized successfully.