Engineering topics
Publications and source records attributed to Yueh, S. H..
Experimental observations of sea ice polarimetric signatures in the Beaufort Sea under cold winter conditions are presented and interpreted with the composite model developed in Part I.
Polarimetric remote sensing of structures in geophysical media is studied in this paper based on their symmetry properties. Orientations of spheroidal scatterers described by spherical, uniform, planophile, plagiothile, erectophile, and extremophile distributions are considered to derive their polarimetric backscattering characteristics. These distributions can be identified from the observed scattering coefficients by comparison with theoretical symmetry calculations. A new parameter is defined to study scattering structures in geophysical media. Experimental observations from polarimetric data acquired by the Jet Propulsion Laboratory airborne synthetic aperture radar over forests, sea ice, and sea surface are presented to illustrate the use of symmetry properties. For forests, the coniferous forest in Mount Shasta area and mixed forests neir Presque Isle show evidence of the centrical symmetry at C band. In sea ice from the Beaufort Sea, multiyear sea ice has a cross-polarized ratio e close to e(sub 0), calculated from symmetry, due to the randomness in the scattering structure. For first-year sea ice, e is much smaller than e(sub 0) as a result of preferential alignment of the columnar structure of the ice. From polarimetric data of a sea surface in the Bering sea, it is observed that e and e(sub 0) are increasing with incident angle and e is greater than e(sub 0) at L band because of the directional feature of sea surface waves. Use of symmetry properties of geophysical media for polarimetric radar calibration is also suggested.
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This paper investigates the theory of polarimetric passive remote sensing of wind-generated sea surfaces. A two-scale polarimetric scattering and emission model of sea surfaces is developed to interpret existing active and passive remote sensing microwave signatures of sea surfaces, and to investigate the potential application of polarimetic radiometry to ocean surface winds.
Recently, it has been observed that the brightness temperatures of sea surfaces correlate with the azimuth angle of the ocean wind vector, indicating that ocean wind direction can not only be retrieved from the microwave backscatter, but also from the brightness temperature measurements. In this paper, comparison of the theoretical result calculated from a two-scale emission and scattering model with the Seasat and SSM/I model functions and aircraft measurements is presented and potential applications of microwave polarimetry are discussed. In our two-scale model, the modified reflectivities of large scale surfaces are calculated by extending the small perturbation method to the second order for small scale perturbation with anisotropic directional spectrum. It was found that the modified reflectivities derived from the second-order scattered field agree excellently well with the results obtained from a Monte Carlo simulation technique which numerically calculates the polarimetric reflectivities of one-dimensional random rough surfaces with a power-law spectrum. Without the second-order correction, the modified reflectivities of the rough surfaces are significantly over-estimated and sign errors are observed in the third and fourth Stokes parameters for thermal emissions. The surface spectrum parameters and two-scale cutoff are selected so that the calculated scattering and emission signatures agree with the reported model functions for Seasat and SSM/I. Subsequently, the polarimetric signatures of sea surfaces are illustrated to indicate the possibility of reducing the number of azimuthal looks required for spaceborne sensors in the remote sensing of ocean wind by using the polarimetric information. Furthermore, it is found that contrary to the dependence of backscattering coefficients on incidence angles, polarimetric brightness temperatures display a stronger wind direction dependence in the near nadir-looking direction than away from nadir. Finally, we discuss the potential of a single-look polarimetric radiometer for wind retrieval.
Polarimetric backscattering from sea ice is presented. Theoretical models for backscattering are first presented for various ice types. Then, theoretical results are compared with experimental data for new thin ice, first-year ice, and multi-year ice. Sea ice is modeled as a layer medium containing random scatterers and rough interfaces. For multi-year sea ice with snow cover, the sea ice layer is modeled as an ice background with embedded spheroidal air bubbles and the snow layer as air with ice grains. The hummocky topography on multi-year ice is characterized by a Gaussian distribution which has an averaging effect on backscattering coefficients. First-year sea ice is described by an ice medium hosting ellipsoidal brine inclusions. These inclusions are oriented preferentially in the vertical direction due to the columnar structure of first-year sea ice. Azimuthally, the orientation of the brine pockets are random corresponding to the random c-axes in sea ice, unless the axes are oriented by sea currents. For thin ice in newly opened leads, it has been observed that there exists a thin brine layer with very high salinity on the top surface of the new ice. This brine layer is depicted as a medium with high permittivity which can significantly affect electromagnetic scattering signatures from the lower thin ice layer, with a higher fractional volume of brine inclusions due to higher salinity as compared to thick first-year sea ice. The rough medium interfaces are described as Gaussian rough surfaces characterized by root-mean-square heights and surface correlation lengths. The contribution from rough surface, calculated under the Kirchhoff approximation or small perturbation method, is assumed to be independent from volume scattering. The total loss including absorption and scattering losses in the scattering media is represented by the imaginary part of effective permittivity obtained from the strong fluctuation theory. The polarimetric scattering coefficients for different ice types are then derived under the distorted Born approximation.
We describe a potential procedure for retrieving ice thickness from multi-frequency polarimetric SAR data for thin ice. This procedure includes first masking out the thicker ice types with a simple classifier and then deriving the thickness of the remaining pixels using a model-inversion technique. The technique used to derive ice thickness from polarimetric observations is provided by a numerical estimator or neural network. A three-layer perceptron implemented with the backpropagation algorithm is used in this investigation with several improved aspects for a faster convergence rate and a better accuracy of the neural network. These improvements include weight initialization, normalization of the output range, the selection of offset constant, and a heuristic learning algorithm. The performance of the neural network is demonstrated by using training data generated by a theoretical scattering model for sea ice matched to the database of interest. The training data are comprised of the polarimetric backscattering coefficients of thin ice and the corresponding input ice parameters to the scattering model. The retrieved ice thickness from the theoretical backscattering coefficients is compare with the input ice thickness to the scattering model to illustrate the accuracy of the inversion method. Results indicate that the network convergence rate and accuracy are higher when multi-frequency training sets are presented. In addition, the dominant backscattering coefficients in retrieving ice thickness are found by comparing the behavior of the network trained backscattering data at various incidence angels. After the neural network is trained with the theoretical backscattering data at various incidence anges, the interconnection weights between nodes are saved and applied to the experimental data to be investigated. In this paper, we illustrate the effectiveness of this technique using polarimetric SAR data collected by the JPL DC-8 radar over a sea ice scene.
Recent theoretical works have suggested the potential of passive polarimetry in the remote sensing of geophysical media. It was shown that the third Stokes parameter U of the thermal emission may become larger for azimuthally asymmetric fields of observation. In order to investigate the potential applicability of passive polarimetry to the remote sensing of ocean surface, measurements of the polarimetric thermal emission from a sinusoidal water surface and a numerical study of the polarimetric thermal emission from randomly rough ocean surfaces were performed. Measurements of sinusoidal water surface thermal emission were performed using a sinusoidal water surface which was created by placing a thin sheet of fiberglass with a sinusoidal profile in two dimensions extended infinitely in the third dimension onto a water surface. The theory of thermal emission from a 'two-layer' periodic surface is derived and the exact solution is performed using both the extended boundary condition method (EBC) and the method of moments (MOM). The theoretical predictions are found to be in good agreement with the experimental results once the effects of the radiometer antenna pattern are included and the contribution of background noise to the measurements is modeled. The experimental results show that the U parameter indicates the direction of periodicity of the water surface and can approach values of up to 30 K for the surface observed. Next, a numerical study of polarimetric thermal emission from randomly rough surfaces was performed. A Monte Carlo technique utilizing an exact method for calculating thermal emission was chosen for the study to avoid any of the limitations of the commonly used approximate methods in rough surface scattering. In this Monte Carlo technique, a set of finite rough surface profiles in two dimensions with desired statistics was generated and extended periodically. The polarimetric thermal emission from each surface of the set was then calculated using both the EBC and the MOM and the results were averaged. The surface statistics chosen were intended to model a wind perturbed ocean surface in the X to Ku band microwave region. The results indicate that the U parameter is sensitive to the azimuthal angle between the surface periodicity and the looking angle and to the rms height of the surface, and that the U parameter is fairly insensitive to variations in polar angle, permittivity, surface power law spectrum, and surface spectrum high frequency cutoff. These properties give further strength to the idea of using the U parameter to detect wind direction over the ocean.
Experimental results and theoretical calculations are presented to study the polarimetric emission from water surfaces with directional features. For our ground-based Ku-band radiometer measurements, a water pool was constructed on the roof of a building in the Jet Propulsion Laboratory, and a fiberglass surface with periodic corrugations in one direction was impressed on the top of the water surface to create a stationary water surface underneath it. It is observed that the measured Stokes parameters of corrugated fiberglass-covered water surfaces are functions of azimuth angles and agree very well with the theoretical calculations. The theory, after being verified by the experimental data, was then used to calculate the Stokes parameters of periodic surfaces without fiberglass surface layer and with rms height of the order of wind-generated water ripples. The magnitudes of the azimuthal variation of the calculated emissivities at horizontal and vertical polarizations corresponding to the first two Stokes parameters are found to be comparable to the values measured by airborne radiometers and SSM/I. In addition, the third Stokes parameter not shown in the literature is seen to have approximately twice the magnitude of the azimuth variation of either T(sub h) or T(sub v), which may make it more sensitive to the row direction, while less susceptive to noises because the atmospheric and system noises tend to be unpolarized and are expected to be cancelled out when the third Stokes parameter is derived as the difference of two or three power measurements, as indicated by another experiment carried out at a swimming pool with complicated surroundings. The results indicate that passive polarimetry is a potential technology in the remote sensing of ocean wind vector which is a crucial component in the understanding of global climate change. Issues related to the application of microwave passive polarimetry to ocean wind are also discussed.
Relationships among polarimetric backscattering coefficients are derived from the viewpoint of symmetry groups. For both reciprocal and non-reciprocal media, symmetry encountered in remote sensing due to reflection, rotation, azimuthal, and centrical symmetry groups is considered. The derived properties are general and valid to all scattering mechanisms, including volume and surface scatterings and their interactions, in a given symmetrical configuration. The scattering coefficients calculated from theoretical models for layer random media and rough surfaces are shown to obey the symmetry relations. Use of symmetry properties in remote sensing of structural and environmental responses of scattering media is also discussed. Orientations of spheroidal scatterers described by spherical, uniform, planophile, plagiothile, erectophile, and extremophile distributions are considered to derive their polarimetric backscattering characteristics. These distributions can be identified from the observed scattering coefficients by comparison with theoretical symmetry calculations. A new parameter is then defined to study scattering structures in geophysical media. Observations from polarimetric data acquired by the Jet Propulsion Laboratory airborne synthetic aperture radar over forests, sea ice, and sea surface are presented. Experimental evidences of the symmetry relationships are shown and their use in polarimetric remote sensing is illustrated. For forests, the coniferous forest in Mt. Shasta area (California) and mixed forest near Presque Isle (Maine) exhibit characteristics of the centrical symmetry at C-band. For sea ice in the Beaufort Sea, multi-year sea ice has a cross-polarized ratio e close to e(sub 0), calculated from symmetry, due to the randomness in the scattering structure. First-year sea ice has e much smaller than e(sub 0) due to the preferential alignment of the columnar structure of the ice. From polarimetric data of a sea surface in the Bering Sea, it is observed that e and e(sub 0) are increasing with incident angle and e is greater than e(sub 0) at L-band because of the directional feature of sea surface waves. Symmetry properties of geophysical media can also be used to calibrate polarimetric radars.
In this paper, theoretical and numerical results of the polarimetric scattering and emission from random rough surfaces with anisotropic directional spectrum are presented for the remote sensing of ocean and soil surfaces. The polarimetric scattered field for rough dielectric surfaces is derived to the second order by the small perturbation method (SPM). It is found that the second-order scattered field is coherent in nature, and its coefficients for different polarizations present the lowest-order corrections to the Fresnel reflection coefficients of the surfaces. In addition, the cross-polarized (HV and VH) components of the coherent fields are reciprocal and not zero for surfaces with anisotropic directional spectrum when the azimuth angle of the incident direction is not aligned with the symmetry directions of surfaces. In order to verify the energy conservation condition of the theoretical results, which is important if the theory is to be applied to the passive polarimetry of rough surfaces, a Monte Carlo simulation is performed to numerically calculate the polarimetric reflectivities of one-dimensional random rough surfaces which are generated with a prescribed power-law spectrum in the spectral domain and transformed to the spatial domain by the FFT. The surfaces simulated by this approach are periodic with the period corresponding to the low-wavenumber cutoff. To calculate the scattering from periodic dielectric surfaces, the authors present a new numerical technique which applies the Floquet theorem to reduce the problem to one period and does not require the evaluation of one-dimensional periodic Green's function used in the conventional method of moment formulation. Once the scattering coefficients are obtained, the polarimetric Stokes vectors for the emission from the random surfaces are then calculated according to the Kirchhoff's law and are illustrated as functions of relative azimuth observation and row directions. The second-order SPM is also reduced to this one-dimensional case, and theoretical results for all four Stokes parameters are in excellent agreement with the numerical results obtained from the Monte Carlo simulation. Moreover, the second order coherent fields are found to be necessary for the theoretical evaluation of the third and fourth Stokes parameters. Without that, the reflectivities of random rough surfaces are significantly over-estimated, and the signs of the third and the fourth Stokes parameters are found to be incorrect, if calculated by using the first-order incoherent fields alone.
This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.
This paper presents the relations among polarimetric backscattering coefficients from the viewpoint of symmetry groups. Symmetry of geophysical media encountered in remote sensing due to reflection, rotation, azimuthal, and centrical symmetry groups is considered for both reciprocal and nonreciprocal cases. On the basis of the invariance under symmetry transformations in the linear polarization basis, the scattering coefficients are related by a set of equations which restrict the number of independent parameters in the polarimetric covariance matrix. The properties derived under these transformations are general and apply to all scattering mechanisms in a given symmetrical configuration. The scattering coefficients calculated from theoretical models for layer random media and rough surfaces are shown to obey the derived symmetry relations. Use of symmetry properties in remote sensing of structural and environmental responses of scattering media is discussed. As a practical application, the results from this paper provide new methods for the external calibration of polarimetric radars without the deployment of man-made calibration targets.
The polarimetric emission from an anisotropic medium with applications to the passive microwave remote sensing of sea ice is investigated. Based on the fluctuation-dissipation theory, the Stokes vector is obtained for the thermally exicted electromagnetic radiation from an anisotropic object. By modeling sea ice as a layered anisotropic medium, the Stokes vector representing the polarimetric emission is illustrated for various geophysical conditions. It is found that the difference in the brightness temperatures measured at two surface temperatures is correlated with the thickness of the sea ice. For cases where the c-axis of sea ice is aligned or not random in the horizontal plane, the calculated brightness temperatures show a periodic variation when plotted as a function of the azimuthal angle of the observation direction, indicating that the polarimetric Stokes parameters can be used to detect the direction of c-axes in sea ice.
Polarimetric calibration algorithms using combinations of point targets and reciprocal distributed targets are developed. From the reciprocity relations of distributed targets, and equivalent point target response is derived. Then the problem of polarimetric calibration using two point targets and one distributed target reduces to that using three point targets, which has been previously solved. For calibration using one point target and one reciprocal distributed target, two cases are analyzed with the point target being a trihedral reflector or a polarimetric active radar calibrator (PARC). For both cases, the general solutions of the system distortion matrices are written as a product of a particular solution and a matrix with one free parameter. For the trihedral-reflector case, this free parameter is determined by assuming azimuthal symmetry for the distributed target. For the PARC case, knowledge of one ratio of two covariance matrix elements of the distributed target is required to solve for the free parameter. Numerical results are simulated to demonstrate the usefulness of the developed algorithms.
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Strong permittivity fluctuation theory is used to solve the problem of scattering from a medium composed of completely randomly oriented scatterers under a low frequency limit. Gaussian statistics are not assumed for the renormalized scattering sources. Numerical results on effective permittivity are illustrated for oblate and prolate spheroidal scatterers and compared with the results for spherical scatterers. The results are consistent with discrete scatterer theory. The effective permittivity of a random medium embedded with nonspherical scatterers shows a higher imaginary part than the spherical scatterer case with equal correlation volume. Under the distorted Born approximation, the polarimetric covariance matrix for the backscattered electric field is calculated for half-space randomly oriented scatterers. The nonspherical geometry of the scatterers shows significant effects on the cross-polarized backscattering returns, and the correlation coefficient between HH and VV returns. The polarimetric backscattering coefficients can provide useful information in distinguishing the geometry of scatterers.
A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar data from earth terrain with polarizations HH, HV, VH, and VV. In this approach, correlated polarizations of radar signals, as characterized by a covariance matrix, are treated as the sum of N n-dimensional random vectors; N obeys the negative binomial distribution with a parameter alpha and mean N-bar. Subsequently, an n-dimensional K-distribution, with either zero or nonzero mean, is developed in the limit of infinite N-bar or illuminated area. The probability density function (PDF) of the K-distributed vector normalized by its Euclidean norm is independent of the parameter alpha and is the same as that derived from a zero-mean Gaussian-distributed random vector.