Averaged Stokes Vector Based Polarimetric SAR Data Interpretation

In this paper, we propose a new polarimetric synthetic aperture radar (SAR) data interpretation method based on a locally averaged Stokes vector. We first propose a method to extract discriminators from all three components of the averaged Stokes vector. Based on the extracted discriminators, we build four physical interpretation layers with ascending priorities, i.e., the basic structure layer, the low-coherence targets layer, the man-made targets layer, and the low-backscattering targets layer. An intuitive final image can be generated by simply stacking the four layers in the priority order. We test the performance of the proposed method over Advanced Land Observing Satellite Phased Array type L-band SAR (ALOS-PALSAR) data. Experimental results show that the proposed method has high interpretation performance, particularly for skew-aligned or randomly distributed buildings and isolated man-made targets such as bridges

Radio Astronomical Polarimetry and Point-Source Calibration

A mathematical framework is presented for use in the experimental determination of the polarimetric response of observatory instrumentation. Elementary principles of linear algebra are applied to model the full matrix description of the polarization measurement equation by least-squares estimation of non-linear, scalar parameters. The formalism is applied to calibrate the center element of the Parkes Multibeam receiver using observations of the millisecond pulsar, PSR J0437-4715, and the radio galaxy, 3C 218 (Hydra A).Comment: 8 pages, 4 figures, to be published in ApJ

Performance of Scattering Matrix Decomposition and Color Spaces for Synthetic Aperture Radar Imagery

Polarimetrc Synthetic Aperture Radar (SAR) has been shown to be a powerful tool in remote sensing because uses up to four simultaneous measurements giving additional degrees of freedom for processing. Typically, polarization decomposition techniques are applied to the polarization-dependent data to form colorful imagery that is easy for operators systems to interpret. Yet, the presumption is that the SAR system operates with maximum bandwidth which requires extensive processing for near- or real-time application. In this research, color space selection is investigated when processing sparse polarimetric SAR data as in the case of the publicly available \Gotcha Volumetric SAR Data Set, Version 1:0 . To improve information quality in resultant color imagery, three scattering matrix decompositions were investigated (linear, Pauli and Krogager) using two common color spaces (RGB, CMY) to determine the best combination for accurate feature extraction. A mathematical model is presented for each decomposition technique and color space to the Cramer-Rao lower bound (CRLB) and quantify the performance bounds from an estimation perspective for given SAR system and processing parameters. After a deep literature review in color science, the mathematical model for color spaces was not able to be computed together with the mathematical model for decomposition techniques. The color spaces used for this research were functions of variables that are out of the scope of electrical engineering research and include factors such as the way humans sense color, environment influences in the color stimulus and device technical characteristics used to display the SAR image. Hence, SAR imagery was computed for specific combinations of decomposition technique and color space and allow the reader to gain an abstract view of the performance differences

Radio Astronomical Polarimetry and High-Precision Pulsar Timing

A new method of matrix template matching is presented in the context of pulsar timing analysis. Pulse arrival times are typically measured using only the observed total intensity light curve. The new technique exploits the additional timing information available in the polarization of the pulsar signal by modeling the transformation between two polarized light curves in the Fourier domain. For a number of millisecond pulsars, arrival time estimates derived from polarimetric data are predicted to exhibit greater precision and accuracy than those derived from the total intensity alone. Furthermore, the transformation matrix produced during template matching may be used to calibrate observations of other point sources. Unpublished supplementary material is appended after the bibliography.Comment: 13 pages, 3 figures, published in ApJ, includes supplementary material that was not submitted to The Astrophysical Journal and has not been peer reviewe

New target detector based on geometrical perturbation filters for polarimetric Synthetic Aperture Radar (POL-SAR)

Synthetic Aperture Radar (SAR) is an active microwave remote sensing system able to acquire high resolution images of the scattering behaviour of an observed scene. The contribution of SAR polarimetry (POLSAR) in detection and classification of objects is described and found to add valuable information compared to previous approaches. In this thesis, a new target detection/classification methodology is developed that makes novel use of the polarimetric information of the backscattered field from a target. The detector is based on a geometrical perturbation filter which correlates the target of interest with its perturbed version. Specifically, the operation is accomplished with a polarimetric coherence representing a weighted and normalised inner product between the target and its perturbed version, where the weights are extracted from the observables. The mathematical formulation is general and can be applied to any deterministic (point) target. However, in this thesis the detection is primarily focused on multiple reflections and oriented dipoles due to their extensive availability in common scenarios. An extensive validation against real data is provided exploiting different datasets. They include one airborne system: E-SAR L-band (DLR, German Aerospace Centre); and three satellite systems: ALOS-PALSAR L-band (JAXA, Japanese Aerospace Exploration Agency), RADARSAT-2 C-band (Canadian Space Agency) and TerraSAR-X X-band (DLR). The attained detection masks reveal significant agreement with the expected results based on the theoretical description. Additionally, a comparison with another widely used detector, the Polarimetric Whitening Filter (PWF) is presented. The methodology proposed in this thesis appears to outperform the PWF in two significant ways: 1) the detector is based on the polarimetric information rather than the amplitude of the return, hence the detection is not restricted to bright targets; 2) the algorithm is able to discriminate among the detected targets (i.e. target recognition)

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space

Ellipticity and Deviations from Orthogonality in the Polarization Modes of PSR B0329+54

We report on an analysis of the polarization of single pulses of PSR B0329+54 at 328 MHz. We find that the distribution of polarization orientations in the central component diverges strongly from the standard picture of orthogonal polarization modes (OPMs), making a remarkable partial annulus on the Poincare sphere. A second, tightly clustered region of density appears in the opposite hemisphere, at a point antipodal to the centre of the annulus. We argue that this can be understood in terms of birefringent alterations in the relative phase of two elliptically polarized propagation modes in the pulsar magnetosphere (i.e. generalised Faraday rotation). The ellipticity of the modes implies a significant charge density in the plasma, while the presence of both senses of circular polarization, and the fact that only one mode shows the effect, supports the view that refracted ordinary-mode rays are involved in the production of the annulus. At other pulse longitudes the polarization (including the circular component) is broadly consistent with an origin in elliptical OPMs, shown here quantitatively for the first time, however considerable non-orthogonal contributions serve to broaden the orientation distribution in an isotropic manner.Comment: 13 pages, 5 figures, to appear in A&

Evaluation of the potential of ALOS PALSAR L-band quadpol radar data for the retrieval of growing stock volume in Siberia

Because of the massive wood trade, illegal logging and severe damages due to fires, insects and pollution, it is necessary to monitor Siberian forests on a large-scale, frequently and accurately. One possible solution is to use synthetic aperture radar (SAR) remote sensing technique, in particular by combining polarimetric technique. In order to evaluate the potentiality of ALOS PALSAR L-band full polarimetric radar for estimation of GSV, a number of polarimetric parameters are investigated to characterise the polarisation response of forest cover. Regardless of the weather conditions, a high correlation (R=-0.87) is achieved between polarimetric coherence and GSV. The coherence in sparse forest is always higher than in dense forest. The coherence level and the dynamic range strongly depends on the weather conditions. The four-component polarimetric decomposition method has been applied to the ALOS PALSAR L-band data to compare the decomposition powers with forest growing stock volume (GSV). Double-bounce and volume scattering powers show significant correlation with GSV. The correlation between polarimetric decomposition parameters and GSV is enhanced if the ratio of ground-to-volume scattering is used instead of considering polarimetric decomposition powers separately. Two empirical models have been developed that describe the ALOS PALSAR L-band polarimetric coherence and ground-to-volume scattering ratio as a function of GSV. The models are inverted to retrieve the GSV for Siberian forests. The best RMSE of 38 m³/ha and R²=0.73 is obtained based on polarimetric coherence. On the other hand, using the ratio of ground-to-volume scattering the best retrieval accuracy of 44 m³/ha and R²=0.62 is achieved. The best retrieval results for both cases are observed under unfrozen condition. Saturation effects for estimated GSV versus ground-truth GSV are not observed up to 250 m³/ha