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.

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.

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.

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.