Thomas L. Ainsworth

The use of bistatic polarimetric SAR for rough surface observation has attracted increasing interest in recent years, with its acquisition of additional polarimetric information. In this paper, we investigate the sensitivity of polarimetric variables to soil moisture and surface roughness, with the intention of locating favorable bistatic geometries for soil moisture retrieval. However, in the bistatic setting, the expanded imaging geometry is convolved with the polarimetric scattering response along with the in-scene variations in the soil moisture and surface roughness. The probing polarization states continuously evolve with the bistatic geometry, incurring varying polarization orientation angles. In this investigation, we propose to first compensate the bistatic polarimetric observations for the geometry-induced polarization rotation. Simulations based on a two-scale rough surface scattering model are then used to evaluate the optimal imaging geometry for the best sensitivity to the soil moisture content. We show the different sensing geometries associated with a full list of common polarimetric variables, as we seek favorable bistatic geometries in non-specular directions. The influences of both surface roughness scales are evaluated, with the small-scale roughness parameter imposing the greatest limitation on our results.

Estimation of polarization orientation angle (POA) is of general interest in polarimetric SAR (PolSAR) image processing because it is useful to remove the orientation-induced polarimetric distortion but also to infer physical target parameters. Under the assumption of scatter reflection symmetry, POA can be readily estimated from a circular polarization basis formulation or equivalently from zeroing the Pauli-basis correlation term ${{\boldsymbol{T}}}_{23}$ . This solution does not account for a possible presence of helical components; alternatively, we developed a general formulation for POA estimation using Touzi parameterization that explicitly contains a helical angle. When applied to PolSAR imagery of rough terrain surface, those two estimators generally give different results. Based on the POA derived from coregistered digital elevation model data, we find the former estimator can underestimate the POA for rough terrain surface. In this article, we further investigate the underlying cause. Although these two POA estimators differ in whether assuming zero helicity or not, the inherent helicity on the rough surface scattering is negligible; instead, the difference in the estimated POA is mainly attributed to the degree of mixing of a secondary scattering mechanism. This mixing impact can be significant even for rough terrain with only light vegetation cover, suggesting a practical need for inclusion of alternative estimators.

Model-based polarimetric decompositions are often used to generate scene classifications from polarimetric synthetic aperture radar (POLSAR) imagery. The classification quality largely depends on whether the adopted polarimetric scattering mechanisms match the major in-scene scattering mechanisms. However, in-scene scattering variations still cause potential mismatches, resulting in unphysical or inaccurate decomposition results. The robustness of model-based polarimetric decompositions remains a general concern. In this article, we address the robustness of model-based polarimetric decomposition to variations of the in-scene scattering mechanisms using simulated datasets. The known simulated scattering mechanisms provide the needed ``ground truth'' against which we quantitatively evaluate the robustness of polarimetric decompositions. Accurate retrieval of known scattering mechanisms from simulated polarimetric data led us to develop a robust model-based decomposition. We propose a new approach to solve model-based decomposition by employing an L₁-regularized optimization procedure, which automatically selects a set of optimal polarimetric scattering mechanisms and guarantees nonnegative powers for the selected scattering mechanisms. We illustrate this L₁ model decomposition by employing both simulated datasets and actual POLSAR imagery. Our new L₁-regularized approach to POLSAR model-based decomposition that mitigates observed biases seen in earlier decompositions provides robust scattering mechanism estimates and eliminates unphysical, negative scattering powers.

Microwave reflectometers provide spectrally integrated information of ocean surface waves several times longer than the incident electromagnetic (EM) wavelengths. For high wind condition, it is necessary to consider the modification of relative permittivity by air in foam and whitecaps produced by wave breaking. This paper describes the application of these considerations to microwave specular returns from the ocean surface. Measurements from Ku and Ka band altimeters and L band reflectometers are used for illustration. The modeling yields a straightforward integration of a closed-form expression connecting the observed specular normalized radar cross section (NRCS) to the surface wave statistical and geometric properties. It remains a challenge to acquire sufficient number of high-wind collocated and simultaneous reference measurements for algorithm development or validation and verification effort. Solutions from accurate forward computation can supplement the sparse high wind databases. Modeled specular NRCSs are provided for L, C, X, Ku, and Ka bands with wind speeds up to 99 m/s.

Polarimetric SAR (PolSAR) imagery offers an enhanced capability to reveal the salient scattering properties of scene content. PolSAR-based target decomposition has been widely used to show different apparent scattering mechanisms for various target classes, empowering a direct yet powerful technique for SAR imagery analysis. Among those common targets, modeling the random volume scattering from vegetation is one of the most important. Generally, one models vegetation as a cloud of randomly oriented thin cylinders, mainly intended for twigs and branches. At high radar frequencies, PolSAR imagery shows a strong response from leaves in the vegetation canopy. In this letter, we derive the polarimetric scattering theory for general random volume scatterers, including both thin cylinders and thin disks as limiting cases for leaf response. Adding the proposed random thin disk model explains the observed scattering difference between deciduous forest and coniferous forest, which we then incorporate into a new model-based PolSAR target decomposition scheme.

We present novel experimental evidence that demonstrates the effectiveness of exploiting scene motion information for the analysis of scene structure in maritime imaging applications. We analyze data captured by our novel airborne Multi-channel SAR (MSAR) system that is particularly suited to sampling the velocity profile of scatterers in the maritime environment. While previous works have shown the utility MSAR systems for correcting scene motion induced blurring artifacts, our work shows, for the first time, how the information furnished by an MSAR system can systematically render accurate classification of maritime scenes into different perceptual categories. We offer a methodology that is superior to traditional classification techniques that are based purely on the spatial structure of an image. Furthermore, the simplicity of the feature space involved together with the demonstrated classification performance on imagery captured by our airborne MSAR system underscore the strength of the methodology.

Surface wave spectral properties of centimeter to decameter (cmDm) wavelengths are of great interest to microwave remote sensing of the ocean. They are obviously different from the high-frequency extension of the wind-wave spectrum models developed for ocean science and engineering applications, which focus on the longer waves in the energetic peak region of the wave spectrum. For more than six decades, the cmDm waves are generally considered to be in the equilibrium range, and its spectral function has a constant slope: -5 or -4 in the 1-D frequency spectrum, and -3 or -2.5 in the 1-D wavenumber spectrum. The observed wind-wave spectral slopes, however, are not constant. As a result, the cmDm wave properties are significantly different from those inferred from an equilibrium spectrum model. Surface slope measurements are more suited for studying the cmDm waves. Microwave radar backscattering cross sections have been used to study the shorter range of cmDm waves. L-band lowpass-filtered mean square slope (LPMSS) is contributed by waves longer than about 0.6 m, here referred to as the decimeter to decameter (dmDm) waves. The analysis of LPMSS has improved the modeling of dmDm waves. Ultimately, the spectral slope variation is a critical characteristic of cmDm waves. The wave spectrum model formulated with the variable spectral slope consideration produces very good agreement with L-band scatterometer and reflectometer measurements.

We have developed a general framework to assess coherent polarimetric change that applies equally to quad-pol, dual-pol and hybrid-pol imagery. The formalism permits estimating polarimetric coherence for any combination of transmitted and received polarizations. In particular, the polarizations can be tuned to match specific targets of interest, or conversely, to determine the scattering properties of the changed targets. We will complete the investigation of both likelihood-ratio test statistics and illustrate our results using RadarSAR-2 and PALSAR polarimetric data. Our results will also be compared to those from previous works.

The advancement of SAR technology with high resolution and quad-polarization data demands better and efficient speckle filtering algorithms. In principle, filtering should be applied to distributed media only. Strong pointed targets should be kept unfiltered. In this paper, we will review some of the recently published PolSAR speckle filtering algorithms, and discuss several important issues that affect the speckle filtering results.