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Polarimetric synthetic aperture radars, operating at L-, C- and P-band, were designed to replace and upgrade a system destroyed in an aircraft accident. Ground and flight tests were conducted, and the radar was flown over a calibration site in a sequence of experiments designed to calibrate the system. The radar also took part in science campaigns.
The use and application of multisensor data sets are an absolute necessity for remote sensing to fulfill its promise. Initial efforts at registering dissimilar images have been made, but results are not widely available. This paper describes some initial work to better understand the difficulties of multisensor registration. Accuracies achieved are in excess of a pixel diameter.
The capability of a single frequency, single polarization radar to distinguish different lava flows solely on the basis of their relative radar backscatter as measured by scatterometer profiles is addressed. It is found that mantled and unmantled flow surfaces can be separated by their radar backscatter and become more distinct with increasing incidence angle. Pristine pahoehoe surfaces have backscatter coefficients distinctly different from older flows at small angles, whereas the most modified pahoehoe units are identifiable at larger incidence angles. At all latitudes on Venus, it should be possible to distinguish the style of volcanism responsible for volcanic plains emplacement.
Synthetic aperture radar illumination requirements for accurate rendition of geological structure in low-relief regions of uniform radar roughness are considered, along with the effect of illumination azimuth biasing. Results of two Shuttle imaging radar Canadian Shield experiments are discussed with emphasis on look azimuth, look direction, and look angle. It is found out that at least two look azimuths, preferably orthogonal, are essential for adequate rendition of regional structure of the area, while four nearly-orthogonal looks are necessary to compensate for slope orientation. This can be accomplished by orbital design with crossing ground tracks or utilizing a squint mode. Attention is given to the acquisition of coverage from look directions 180 deg apart and to multiple look angles desirable for geological applications.
An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.
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The statistical characteristics of image speckle are reviewed. Existing segmentation techniques that have been used for speckle filtering, edge detection, and texture extraction are sumamrized. The relative effectiveness of each technique is briefly discussed.
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A theoretical analysis of polarization filtering for the bistatic case is developed for optimum discrimination between two types of targets. The resulting method is half analytical and half numerical. Because it is based on the Stokes matrix representation, the targets of interest can be extended targets. The scattered field from such targets is partially polarized. This method is then applied to the monostatic case with numerical examples relying on the JPL (Jet Propulsion Laboratory) full-polarimetric L-band radar data. A matched filter to maximize the power ratio between urban and natural targets is developed. The results show that the same filter is optimal for both ocean and forest targets as natural targets.
Synthetic aperture radar utilizes coherent microwaves to produce images of the earth's surface. Due to the interference of coherent wavelets, the images appear speckled. This reduces the performance of per-pel classifiers. One way to increase the performance is to filter the image first, then classify the filtered image. For this purpose, several novel filters that have been reported in the literature are investigated. These are the geometric filter, adaptive LMMSE filter, and linear approximation filter. For comparison, conventional mean and median filters are also considered. It is found that the mean filter with seven iterations gives the best result. The overall performance increased from 65.2 to 88.9 percent. The capability of these filters to preserve edges in the original image are also assessed. It is seen that the geometric and median filters are the best in preserving edges, and that the linear approximation and adaptive LMMSE filters are the best in discriminating roads. Prefiltering the image effectively provides contextual information to the per-pel classifier. An alternate approach is to directly design a contextual classifier. A new contextual classifier based on sequential decision theory is proposed. With this classifier, it is found that the overall performance increases to 89.5 percent.
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In improved scheme for Bayesian classification of picture elements in polarimetric synthetic-aperture radar image of terrain, priori probability that given picture element belongs to given class, adjusted according to spatial variation of statistical properties of image data. Accuracy increases dramatically in first few iterations. Scheme involves sequence of classifications. In first, a priori probability that element belongs to class taken to be constant over the whole image. In subsequent classifications, adaptive a priori probabilities calculated for each picture element.
Several techniques to quantitatively analyze the information in the polarimetric signature are discussed, including: (1) a shape (texture) parameter; (2) fractional polarization; (3) the phase difference signature; and (4) the correlation coefficient. These techniques are applied to airborne synthetic aperture radar imagery collected over several different vegetation communities, including a mangrove swamp, a mixed-age loblolly pine forest, and a flooded bald cypress forest.
Principal components analysis of ideal, complex quadpolarized synthetic aperture data tells us how the information is encoded in the eigenstructure of the data from homogeneous, distributed targets. Comparison with real data indicates distortion of the correlation matrices by system effects. The implications for measurement of the terms in the true correlation matrix are displayed. These are related to how accurately we can make inferences about the products of principle components analysis.
The information content of the compressed Stoke's matrix data from the Airborne Synthetic Aperture Radar (AIRSAR) is examined in two ways - by measuring how each feature separates classes of terrain in an image, and by measuring how well a classifier performs with and without each feature. In this way, the features may then be ranked in order of information content (or in order of utility to the classifier). Suggestions are made regarding those variables that can be omitted in a data compression scheme or in a future simplified radar system.
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An experiment was performed with the NASA/JPL airborne radar polarimeter in Sept. 1989 in an agricultural area near Fresno, CA. Based on 1st-order surface backscattering models, a physically based algorithm for retrieval of soil moisture and surface roughness was developed. It is shown that copolarization ratio is sensitive to soil moisture but not to soil roughness at high incidence angles (between 40 and 60 deg). The derived soil moisture was employed to derive a two-layer heat and energy flux model to estimate evaporation from bare soils.