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Van Zyl, J. J.

Publications and source records attributed to Van Zyl, J. J..

9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Estimation of Soil Moisture with L-band Multi-polarization Radar

Through analyses of the model simulated data-base, we developed a technique to estimate surface soil moisture under HYDROS radar sensor (L-band multi-polarizations and 40deg incidence) configuration. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12,0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations.

Shi, J.↗
NASA NTRS1992

Bayesian classification of polarimetric SAR images using adaptive a priori probabilities

The problem of classifying earth terrain by observed polarimetric scattering properties is tackled with an iterative Bayesian scheme using a priori probabilities adaptively. The first classification is based on the use of fixed and not necessarily equal a priori probabilities, and successive iterations change the a priori probabilities adaptively. The approach is applied to an SAR image in which a single water body covers 10 percent of the image area. The classification accuracy for ocean, urban, vegetated, and total area increase, and the percentage of reclassified pixels decreases greatly as the iteration number increases. The iterative scheme is found to improve the a posteriori classification accuracy of maximum likelihood classifiers by iteratively using the local homogeneity in polarimetric SAR images. A few iterations can improve the classification accuracy significantly without sacrificing key high-frequency detail or edges in the image.

Van Zyl, J. J.↗
NASA NTRS1989

Classification of earth terrain using polarimetric synthetic aperture radar images

Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.

Lim, H. H.↗
NASA NTRS1989

Compression Of Data In Imaging Radar Polarimetry

Algorithms developed to reduce number of radar polarimetric data processed to synthesize image of arbitrary combination of transmitting and receiving polarizations. Brings image-processing requirements within computing capabilities of typical users, without degrading images excessively. In scattering-matrix approach to reduction of image data, four adjacent picture elements combined into one by synthesizing new scattering matrix from scattering matrices of four elements. In phase-matrix approach, phase matrices generated from scattering matrices of four adjacent picture elements, and four phase matrices added to combine four picture elements into one.

Zebker, H. A.↗
NASA NTRS1989

Segmentation of multifrequency polarimetric radar images to facilitate the inference of geophysical parameters

An unsupervised clustering algorithm is used to segment multifrequency polarimetric radar data from the NASA/JPL airborne SAR (synthetic aperture radar). Twenty-two parameters are evaluated for their discriminatory capability for each pixel of an image. A clustering analysis is then performed using different subsets of these parameters. This analysis relies on data taken as part of an intensive field experiment during the summer of 1988 in the vicinity of the Pisgah lava flow in the Mojave Desert in southern California. As part of the experiment, extensive ground truth was acquired, including dielectric constant and topography measurements. Segmentation results show good agreement with these measurements.

Burnette, C F.↗
NASA NTRS1989

Inference of geophysical parameters from multifrequency polarimetric radar observations and model inversion

In summer 1988 an intensive field experiment was conducted in the vicinity of the Pisgah lava flow in the Mojave Desert. Physical properties such as microtopography, composition, soil moisture, and dielectric constant were measured at five sites representing surfaces with rms heights varying from less than one centimeter to tens of centimeters. In addition, polarimetric radar images at P-band, L-band, and C-band were acquired at three different incidence angles with the NASA/JPL airborne imaging radar polarimeter. Using trihedral corner reflectors deployed in the area prior to imaging, the L- and C-band images were calibrated to provide sigma0 values for each resolution element in the scene. The results of inferring geophysical parameters such as rms surface height and correlation length of the measured surfaces by fitting the observed signatures with those predicted by the small-perturbation model are presented: for smoother surfaces, the rms height values inferred are in good agreement with in situ measurements.

Van Zyl, J. J.↗
NASA NTRS1985

Modelling of backscatter from vegetation layers

A simple way to build up a library of models which may be used to distinguish between the different types of vegetation and ground surfaces by means of their backscatter properties is presented. The curve of constant power received by the antenna (Gamma sphere) is calculated for the given Stokes Scattering Operator, and model parameters are adopted of the most similar library model Gamma sphere. Results calculated for a single scattering model resembling coniferous trees are compared with the Gamma spheres of a model resembling tropical region trees. The polarization which would minimize the effect of either the ground surface or the vegetation layer can be calculated and used to analyze the backscatter from the ground surface/vegetation layer combination, and enhance the power received from the desired part of the combination.

Van Zyl, J. J.↗