For ocean observation, the wind velocity over water is one major concern. Spaceborne microwave scatterometers can
provide global, all-day, all-time, high-accuracy, high-resolution and short cycle wind velocity observations over the
earth's bodies of water. For a microwave scatterometer system, backscattering coefficient accuracy and spatial resolution
are two important parameters. And they are used to evaluate the performance of a scatterometer. High quality
scatterometer data intends to have both high accuracy measurement of backscattering coefficient and high resolution.
However, these two important parameters are restricted by each other, and cannot reach optimal level at the same time.
Therefore, a compromise between the two variables is necessary for the system design of a scatterometer.
In this paper, simulation results of conically scanned pencil beam scatterometers are presented. Analysis of
backscattering coefficient measurement accuracy under different spatial resolution conditions is also presented. With the
same instrument parameters, larger spatial resolution will increase the number of independent samples of backscattering
measurement. It is well known that the backscattering coefficient accuracy of scatterometers is decided by the SNR of
returned signal and number of independent samples. And simulation results show that the number of independent
samples plays a more important role in backscattering coefficient accuracy than SNR of the returned signal. As a result,
backscattering measurement accuracy and accuracy of retrieved wind velocity can be improved. The simulation results
and analysis can be of benefit to the system design of next generation spaceborne pencil beam scatterometers.
High quality images of Earth produced by synthetic aperture radar (SAR) systems have become increasingly
available, however, SAR images are difficult to interpret. Speckle reduction remains one of the major issues in
SAR imaging process, although speckle has been extensively studied for decades. Many reconstruction filters
have been proposed and they can be classified into two categories: multilook and/or minimum mean-square
error (MMSE) despeckling using the speckle model; and maximum a posteriori (MAP) or maximum likihood
(ML) despeckling using the product model. The most well known Lee, Kuan, and Frost filters belong to first
category. These filters are based on conventional techniques that were originally derived for stationary signals,
such as MMSE. In the second category, filters are based on the product model, such as the MAP Gaussian filter
and the Gamma filter, and require knowledge of the a priori probability density function. These filters force
speckle to have nonstationary Gaussian or gamma distributed intensity mean. The speckle filtering is mainly
Bayesian model fitting that optimizes the MAP criteria. Scene reconstruction is performed using an inversion
of the ascending chain. An objective measure is required to compare the technical merits of these filters, and
Shi et al. presented a comparison 15 years ago. In this paper, a brief introduction of speckle, product, and filter
models is summarized. A review of some most widely used SAR image speckle filters is given. And stationary
speckle filters, like Lee, Kuan, and Frost filters, and nonstationary speckle filters like Gamma MAP filter are
studied. Despeckling results on stationary and nonstationary SAR image of these speckle filters are presented.