Discrimination potential of X-band polarimetric SAR data

In anticipation of X-band polarimetric Synthetic Aperture Radar (SAR) data from future sensors, we investigated the potential of X-band fully polarimetric data for discriminating between the principal classes present in a study site near Avignon, France. Decomposition and analysis techniques have been applied to a dataset acquired by the ONERA airborne RAMSES (Radar Aéroporté Multi-Spectral d'Etude des Signatures) SAR. Results show that X-band provides some discrimination capability. The polarimetric parameters, entropy and α-angle, show clearly that these signature classes are grouped into five clusters corresponding to physical scattering characteristics. The introduction of the anisotropy parameter does not increase our ability to distinguish between different classes whose clusters are in the same entropy/α-angle zone. The correlation observed between the radar signal and the surface roughness over bare soils is very low.     

Damage assessment in urban areas using post-earthquake airborne PolSAR imagery

Synthetic aperture radar (SAR) has often been used in earthquake damage assessment due to its extreme versatility and almost all-weather, day-and-night capability. In this article, we demonstrate the potential to use only post-event, high-resolution airborne polarimetric SAR (PolSAR) imagery to estimate the damage level at the block scale. Intact buildings with large orientation angles have a similar scattering mechanism to collapsed buildings; they are all volume-scattering dominant and reflection asymmetric, which seriously hampers the process of damage assessment. In this article, we propose a new damage assessment method combining polarimetric and spatial texture information to eliminate this deficiency. In the proposed method, the normalized circular-pol correlation coefficient is used first to identify intact buildings aligned parallel with the flight direction of the radar. The ‘homogeneity’ feature of the grey-level co-occurrence matrix (GLCM) is then introduced to distinguish building patches with large orientation angles from the severely damaged class. Furthermore, a new damage assessment index is also introduced to handle the assessment at the level of the block scale. To demonstrate the effectiveness of the proposed approach, the high-resolution airborne PolSAR imagery acquired after the earthquake that hit Yushu County, Qinghai Province of China, is investigated. By comparison with the damage validation map, the results confirm the validity of the proposed method and the advantage of further improving the assessment accuracy without external ancillary optical or SAR data.     

Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry

Forest canopy height is a critical parameter in better quantifying the terrestrial carbon cycle. It can be used to estimate aboveground biomass and carbon pools stored in the vegetation, and predict timber yield for forest management. Polarimetric SAR interferometry (PolInSAR) uses polarimetric separation of scattering phase centers derived from interferometry to estimate canopy height. A limitation of PolInSAR is that it relies on sufficient scattering phase center separation at each pixel to be able to derive accurate forest canopy height estimates. The effect of wavelength-dependent penetration depth into the canopy is known to be strong, and could potentially lead to a better height separation than relying on polarization combinations at one wavelength alone. Here we present a new method for canopy height mapping using dual-wavelength SAR interferometry (InSAR) at X- and L-band. The method is based on the scattering phase center separation at different wavelengths. It involves the generation of a smoothed interpolated terrain elevation model underneath the forest canopy from repeat-pass L-band InSAR data. The terrain model is then used to remove the terrain component from the single-pass X-band interferometric surface height to estimate forest canopy height. The ability of L-band to map terrain height under vegetation relies on sufficient spatial heterogeneity of the density of scattering elements that scatter L-band electromagnetic waves within each resolution cell. The method is demonstrated with airborne X-band VV polarized single-pass and L-band HH polarized repeat-pass SAR interferometry using data acquired by the E-SAR sensor over Monks Wood National Nature Reserve, UK. This is one of the first radar studies of a semi-natural deciduous woodland that exhibits considerable spatial heterogeneity of vegetation type and density. The canopy height model is validated using airborne imaging LIDAR data acquired by the Environment Agency. The rmse of the LIDAR canopy height estimates compared to theodolite data is 2.15 m (relative error 17.6%). The rmse of the dual-wavelength InSAR-derived canopy height model compared to LIDAR is 3.49 m (relative error 28.5%). From the canopy height maps carbon pools are estimated using allometric equations. The results are compared to a field survey of carbon pools and rmse values are presented. The dual-wavelength InSAR method could potentially be delivered from a spaceborne constellation similar to the TerraSAR system.     

Unsupervised classification using polarimetric interferometric decomposition and Wishart classifier

ABSTRACT

In this paper, a decomposition scheme of the coherency matrix is presented to parse the information of polarimetric interferometric synthetic aperture radar (PolInSAR) images in detail. First, the decomposition method is improved by the polarimetric interferometric similarity parameter (PISP) to relief the overestimation occurred in the traditional four-component decomposition method. Second, after using the improved four-component decomposition results as the original inputs, the decomposition method is applied to retrieve scattering mechanisms or identify scatters, with the image separated into seven subsections. Finally, based on the modified decomposition results, the basic classification results are regarded as the feature training sets, and the Wishart classifier is then used as an optimized classification process. The applications of the decomposition and classification scheme are shown with typical representative L-band E-SAR images, which are used to show the robustness of the method, as well as with the first published airborne C-band PolInSAR data collected by the Institute of Electronics, Chinese Academy of Sciences, in November 2017. Experimental results demonstrate that the obtained decomposition and classification results are in good agreement with the actual physical scattering mechanisms.     

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.     

Feature selection and weighted SVM classifier-based ship detection in PolSAR imagery

Target decomposition is an important method for ship detection in polarimetric synthetic aperture radar (SAR) imagery. Parameters such as the polarization entropy and alpha angle deduced from the coherency matrix eigenvalue decomposition capture the differences between the target and background from different views separately. However, under the conditions of a relatively high resolution and a rough sea, the contrast between ship and sea reduces in the aforementioned space. Based on the analyses of target decomposition theory and the target’s scattering mechanism, multi-polarization parameters can be used to characterize different scattering behaviours of the ship target and sea clutter. Moreover, each parameter has its own diverse significance in the practical detection problem. This article proposes a feature selection and weighted support vector machine (FSWSVM) classifier-based algorithm to detect ships in polarimetric SAR (PolSAR) imagery. First, the method constructs a feature vector that consists of multi-polarization parameters. Then, different polarization parameters are refined and weighted according to their significance in the support vector machine (SVM) classifier. Finally, ships are classified from the sea background and other false alarms by the classifier. The validation results on National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) airborne synthetic aperture radar (AIRSAR) and Radarsat-2 quad polarimetric data illustrate that the method detects ship targets more precisely and reduces false alarms effectively.     

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

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

A quad polarimetric SAR calibration algorithm using rotation symmetry

Polarimetric calibration, precisely deciphering the polarimetric information hidden in the polarized characteristics of a radar scene, is the core step before applying quad polarimetric synthetic aperture radar (PolSAR) data for ground parameter inversion and classification. The previously published techniques for polarimetric calibration generally use at least one known point calibration target to completely determine the polarimetric distortions (cross-talks and channel imbalances) between the various polarized channels. This paper describes a novel method that solely relies on the image itself with the property of rotation symmetry. The algorithm derives the entire cross-talk and channel imbalance parameters using an iterative operation to circularly modify the observed average covariance matrix, which is independent on the known point calibration targets in the illuminated scene. The proposed method is validated to be reliable and efficient through polarimetric calibration experiments using the airborne C-band and RadarSat-2 quad polarimetric SAR images. The experimental results indicate that the new technique achieves similar calibration precision to the Ainsworth algorithm but without using any known calibration point target.     

Crop discrimination based on polarimetric correlation coefficients optimization for PolSAR data

Crop discrimination is a necessary step for most agricultural monitoring systems. Radar polarimetric responses from various crops strongly relate to the types and orientations of the local scatterers, which makes the discrimination still difficult using the polarimetric synthetic aperture radar (PolSAR) technique. This work provides a new approach by investigating and utilizing the characteristics of polarimetric correlation coefficients in the rotation domain along the radar line of sight. The theoretical basis lies in that polarimetric correlation coefficients can reflect the different responses and can be enhanced at different levels for various land-cover types with suitable rotation angles in the rotation domain. In this vein, a polarimetric correlation coefficient optimization framework is established and new polarimetric features are extracted therein. Demonstration with multi-frequency (P-, L-, and C-bands) airborne synthetic aperture radar (AIRSAR) PolSAR data over crop areas validates that polarimetric correlation coefficients are crop dependent and the optimized polarimetric correlation coefficient parameters can better discriminate them. Then, a crop discrimination scheme is proposed using the derived polarimetric features. A flow chart for the optimal discrimination feature set selection and determination is provided and is validated by the real data with seven typical crop types. All these crop types are successfully discriminated for the P- and L-band data, whereas only two types of crops are slightly overlapped in the feature space for the C-band data. Experimental studies demonstrate the efficiency and potential of the established methodology.     

A new algorithm for forest height estimation based on the varied extinction random volume over ground (VERVoG) model using PolInSAR data

ABSTRACT

This paper examines a simple geometrical method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The suggested method estimates the forest biophysical parameters based on the varied extinction random volume over ground (VERVoG) model with top layer extinction greater than zero. We approach the problem using a geometrical method without the need for any auxiliary data or prior information. The biophysical parameters, i.e. top layer extinction value, forest height and extinction gradient are estimated in two separate stages. In this framework, the offset value of the extinction is estimated in an independent procedure as a function of a geometrical index based on the signal penetration in the volume layer. As a result, two remaining biophysical parameters can be calculated in a geometrical way based on the observed volume coherence. The proposed algorithm was evaluated using the L-band PolInSAR data of the European Space Agency (ESA) BioSAR 2007 campaign. A pair of experimental SAR (ESAR) images was acquired over the Remningstorp test site in southern Sweden. The selected images were employed for the performance analysis of the proposed approach in the forest height estimation application based on the VERVoG model. The experimental result shows that the proposed inversion method based on the VERVoG model with top layer extinction greater than zero estimates the volume height with an average root mean square error (RMSE) of 2.08 m against light detection and ranging (LiDAR) heights. It presents a significant improvement of forest height accuracy, i.e. 4.1 m compared to the constant extinction RVoG model result, which ignores the forest heterogeneity in the vertical direction.     

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.     

Environmental factors and the detection of open surface water areas with X-band radar imagery

The ability to map open surface water is integral to many hydrologic and agricultural models, wildlife management programmes, and recreational and natural resource studies. Open surface water is generally regarded as easily detected on radar imagery. However, this view is an oversimplification. This study used X-band HH polarized airborne Synthetic Aperture Radar ( SAR) imagery to examine the potential of SAR data to map open fresh water areas extant on 1:100000 USGS topographic maps. Seven study sites in the U.S.A. with a combined area of over 68000km²were analysed. Detection accuracies and minimum size for detection varied among the seven locations. Size and shape of water bodies and radar shadow all affected detection. However, environmental modulation factors including vegetation and forest cover, moisture, and landscape composition and morphology had the greatest influence and exhibited the most complex role in explaining variability     

Removal of azimuth ambiguities and detection of a ship: using polarimetric airborne C-band SAR images

Synthetic aperture radar (SAR) imagery from the sea can contain ships and their ambiguities. The ambiguities are visually identifiable due to their high intensities in the low radar backscatter background of sea environments and can be mistaken as ships, resulting in false alarms in ship detection. Analysing polarimetric characteristics of ships and ambiguities, we found that (a) backscattering from a ship consisted of a mixture of single-bounced, double-bounced and depolarized or diffused scattering types due to its complex physical structure; (b) that only a strong single- or double-bounce scatterer produced ambiguities in azimuth that look like relatively strong double- or single-bounce scatterers, respectively; and (c) that eigenvalues corresponding to the single- or double-bounce scattering mechanisms of the ambiguities were high but the eigenvalue corresponding to the depolarized scattering mechanisms of the ambiguities was low. With these findings, we proposed a ship detection method that applies the eigenvalue to differentiate the ship target and azimuth ambiguities. One set of C-band JPL AIRSAR (Jet Propulsion Laboratory Airborne Synthetic Aperture Radar) polarimetric data from the sea have been chosen to evaluate the method that can effectively delineate ships from their azimuth ambiguities.     

Effects of radar system parameters,population, and environmental modulation on settlement visibility

Abstract.

The detectability of settlements and factors influencing their visibility are explored using imagery from two side-looking airborne radar systems. K-band and X-band imagery of diverse areas in the United States are examined to discover the minimum population needed for a settlement to be consistently detected. The percentage of settlements visible by size of population are calculated and omission/commission errors analysed. Particular attention is devoted to the effects of environmental modulation and a near-, mid-, or far-range location, but the factors of scale, resolution, and system are also addressed.     

Multi-chromatic analysis polarimetric interferometric synthetic aperture radar (MCA-PolInSAR) for urban classification

One of the problems of Synthetic Aperture Radar (SAR) polarimetric decomposition, is that oriented urban areas and vegetation signatures are decomposed into the same volume scattering mechanism. Such indetermination makes it difficult to distinguish vegetation from the oblique urban areas with respect to the radar illumination direction within the volume scattering mechanism. This event occurs because oriented targets exhibit similar polarimetric responses. This paper presents an improvement of the PolSAR decomposition scheme which permits the performing of more accurate classification. The method uses the information existing form the interference generated between two Doppler sub-aperture SAR images. This interferometric polarimetric SAR (PolInSAR) multi-chromatic analysis (MCA-PolInSAR) signal processing method permits the efficient separation of oriented buildings from vegetation yielding considerably improved results in which oriented urban areas are recognized, from volume scattering, as double-bounce objects. Results also show a considerable improvement in the robustness of classification and also in terms of definition and precision.     

Residual motion estimation with point targets and its application to airborne repeat-pass SAR interferometry

Due to the lack of accuracy in the navigation system, airborne repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) suffers residual motion errors (RMEs). Previously, we proposed the multisquint technique with point targets (MTPT) to estimate the errors, but its implementation needs improvement and its accuracy has not been assessed by real airborne repeat-pass InSAR data. In this article, the modified MTPT is introduced first. The modification is mainly embodied in two aspects: automatic target selection and noise removing. Because the multisquint (MS) technique is also capable of estimating the RMEs in SAR interferogram and its accuracy has been verified to be high, here it is used as a comparison. In addition, from the viewpoint of MTPT, MS can be understood from another perspective. Using real X-band airborne repeat-pass SAR data, the performance of MTPT is evaluated. The experiment shows that MTPT is able to achieve high accuracy when a large number of point targets are distributed in the observed scene. The limitations of MTPT are also discussed at the end of this article.     

Anisotropic analysis of polarimetric scattering and case studies with UAVSAR images

This article analyses the anisotropy of polarimetric scattering changing with azimuth incidence angle using a multi-look processed synthetic aperture radar (SAR) image. First, three canonical scattering models were developed to simulate the migration tracks on the Cameron polarimetric space. The migration tracks indicate that these polarimetric parameters have anisotropic property. Second, unmanned aerial vehicle synthetic aperture radar (UAVSAR) data are used to validate the simulated results. The Cameron scattering-type parameter z and the orientation angle calculated by SAR data are consistent with the simulated results by small perturbation method (SPM) double-scattering. Finally, based on the anisotropic analysis, a new method of extracting polarimetric information is proposed. Using this method, six parameters were obtained and two additional parameters, Purity and Stability, were derived. These parameters contain specific physical meaning and are useful in the recognition of the scattering mechanism. Purity can be used to recognize the simple structure scatterers with zero orientation. Stability has the potential to describe the dynamic property of scatterers.     

Improving SAR data processing with polarimetric reference functions in the range Doppler algorithm

Synthetic aperture radar (SAR) is a form of radar that can be used to create images of objects and landscapes. The main important application of the polarimetric SAR can be found in surface and target decomposition process of its image processing. In this article, we propose a method of polarimetric SAR data processing using two new polarimetric reference functions of canonical targets with the intention to apply in coherent decompositions. Our experiment uses polarimetric backscatter characteristics of the dihedral and trihedral reflectors as the targets under a ground-based SAR geometry to create the polarimetric reference functions for azimuth compression in the SAR data processing. We process the data using Pauli decomposition to investigate the effect of our functions on the RGB (red, green, and blue) properties of the processed images. The results show that Pauli decomposition using our functions produces images with different distribution and intensity of RGB colours in the image pixels with some signs of improvement over the traditional range Doppler algorithm. This demonstrates that our polarimetric reference function can be used in the decomposition steps of the traditional SAR data processing and can potentially be used to reveal some useful quantitative physical information of target points of interest and improve image and surface classification.     

Object-based image analysis of optical and radar variables for wetland evaluation

Optical and radar imagery has been shown to be useful for classifying wetland types and surrounding non-wetland classes such as forest and agriculture. Throughout the literature, recommendations have been made that optical and radar image variables together should improve overall and individual class accuracies. object-based image analysis (OBIA) uses multiple data types to segment objects representing land cover entities that are subsequently classified. There are few studies that have utilized optical and polarimetric radar variables together in OBIA to map wetland classes. This research investigated the potential to combine WorldView-2 optical image variables with fully polarimetric Radarsat-2 image variables in OBIA classification of wetland type. With the addition of radar polarimetric variables, classification accuracy improved for the wetland classes of fen, bog, and swamp over the use of optical imagery alone; specifically the addition of Cloude–Pottier (CP) variables of entropy, anisotropy, and alpha angle improved the classification of fen, and the addition of horizontal transmit and horizontal receive (HH) and horizontal transmit and vertical receive (HV) backscatter intensity improved the classification of swamp.     

On the exploitation of polarimetric SAR data to map damping properties of the Deepwater Horizon oil spill

A polarimetric scattering model is proposed to exploit quad-polarimetric synthetic aperture radar (SAR) data to both observe surfactants at sea and provide the first information on the spatial variability of their damping properties. The model is based on the departure from the clean sea surface Bragg/tilted Bragg scattering mechanism. This departure is shown to be a function of the surfactant’s characteristics, and therefore, it is exploited to map them. Case studies of polarimetric SAR data collected during the Deepwater Horizon oil spill in Gulf of Mexico are examined. The approach is robust enough to successfully exploit both L-band airborne and C-band satellite SAR data. This is of paramount importance, even operationally, since it makes this physical approach cross-sensors and, therefore, suitable to exploit all the operational polarimetric missions, thus allowing a denser spatial/temporal coverage.