As a typical next-generation radar system, multistatic radar operates with distinct transmit and receive antennas, which can obtain three or more simultaneous echo datasets [1] . Such radar system has great potential in increasing the capability, reliability and flexibility of future SAR missions [2] . Recent years, various spaceborne multistatic SAR configurations have been introduced [3] , [4] , and the datasets are relatively scant due to the difficulty in constellation configurations. A multistatic radar system was developed in the anechoic chamber of Laboratory of Target Microwave Properties in 2021, which supposed to provide large amounts of radar cross section (RCS) and imaging data in the future. The amplitude of the RCS was calibrated using a metallic sphere, and the accuracy of the multistatic radar system was validated, which demonstrated the capability of the system.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. One of the reasons is that the study of SAR imaging processing, SAR image processing and SAR applications are usually conducted respectively, and the study on integrating the three processes is lacked. Focusing on the science problem, taking the typical natural distribution targets (surface deformation, sea ice classification) and man-made targets (building complex and collapsed buildings) as examples, some application studies oriented to SAR environmental parameters inversion are conducted, and the information integrated frames and methods are proposed.

The Goldstein filter is one of the most commonly used filters for synthetic aperture radar (SAR) interferograms. The level of noise after filtering is controlled by a filter parameter, “alpha,” the value of which is determined by pixels within the moving window. However, when there exist different features within a single filter window, especially along the border, the value of alpha as estimated from the pixels within the window can be inaccurate and this may result in blurred borders in filtered interferograms. This letter proposes a modified Goldstein filter based on the adaptive-neighborhood technique. The idea of this method is to filter each pixel of the interferogram within an adjusted filter patch. In this adjusted patch, the adaptive-neighborhood pixels retain the original phase values while the “background” pixels are replaced by the mean value of adaptive-neighborhood pixels. Then, the Fourier transform of the complex phase is applied to this adjusted filter patch. The difficulty of estimating the noise level near the borders of different features can be decreased using this new filtering method. The quantitative results from real data show that this newly developed method could reduce the phase noise efficiently while also outperforming the Goldstein, Baran and empirical mode decomposition (EMD) filters by preserving the edges in interferograms.

This paper presents the experimental results regarding changes in the dielectric properties of artificially moistened and salinized soils and on soil samples taken from a salt lake. The complex dielectric constants of soil samples were measured using a microwave network analyzer. We evaluated the real and imaginary parts of dielectric constants of artificially moistened and salinized soil samples prepared in the laboratory as a function of microwave frequency, salinity, and water content The frequency and the salinity of soils have little influence on the real part of dielectric constant /spl epsiv/'. The results show that, in the frequency range of 1-6 GHz, the imaginary part /spl epsiv/" has greater sensitivity to soil salinity. The dielectric constant measurements for soil samples collected in Jilantai Salt Lake are in agreement with the results of the artificially moistened and salinized laboratory soil samples. These dielectric measurements were subsequently compared with the backscattering coefficients extracted from a RADARSAT image (C-HH) that was acquired at same time with the soil sampling at the Jilantai Salt Lake area. We discovered that the correlation coefficient between /spl sigma//sup 0/ extracted from the RADARSAT image and /spl epsiv/" measured in the soil sample is 0.70. The correlation coefficient between /spl sigma//sup 0/ and the soil sample salinity is 0.69. This suggests that soil salinity has a significant contribution to the backscattering coefficient, /spl sigma//sup 0/ recorded in a SAR image. Consequently, a SAR image can be a useful tool for monitoring soil salinity.

The major goals of the variable SARs were applications in ocean wave research and wave forecasting. Two-dimensional ocean wave spectra can be derived from SIR-C/X-SAR images by inversion of the SAR imaging mechanism. But for a composite sea surface, i.e., there exist local wind-generated waves, long wave (or swell) from far fetch, internal waves and/or wave breakers, to separate very certain wave information or wave spectrum is difficult in practice. Inversion of a 2D spectrum of internal wave surface of South-China Sea is computed from SIR-C/X-SAR image data. A new inversion method is presented which estimates two dimensional wave spectra. The scheme uses prior information from ocean wave models to add missing information and to account for the fact that the SAR ocean wave imaging mechanism model is not a one to one imaging process. The scheme takes the shape of different ocean wave components as information and uses the SAR information to adjust the mean wavelengths, propagation directions and waveheights of the prior spectrum. The scheme is based on stochastic models for both the measurement error and the prior distribution. The method can separate internal wave from wind-generated waves efficiently.

This paper presents the experimental results regarding changes in the dielectric properties of artificially moistened and salinized soils and on soil samples taken from a salt lake. The complex dielectric constants of soil samples were measured using a microwave network analyzer. We evaluated the real and imaginary parts of dielectric constants of artificially moistened and salinized soil samples prepared in the laboratory as a function of microwave frequency, salinity, and water content. The soil moisture strongly affected the real part of dielectric constant /spl epsiv/'. The frequency and the salinity of soils have little influence on the real part of dielectric constant /spl epsiv/'. The imaginary part of the dielectric constant /spl epsiv/'' is strongly affected by both the salinity and moisture of soil samples, especially at low frequencies. When f<2 GHz, the imaginary part /spl epsiv/'' decreases with each increment of frequency; in contrast, at higher frequencies, /spl epsiv/'' tends toward a constant. The results show that in the frequency range of 1-6 GHz, the imaginary part /spl epsiv/'' has greater sensitivity to soil salinity. Therefore, C and L band are more suitable for soil salinity detection, but L band is better than C band. The dielectric constant measurements for soil samples collected in Jilantai Salt Lake are in agreement with the results of the artificially moistened and salinized laboratory soil samples. These dielectric measurements were subsequently compared with the /spl sigma//sup 0/ backscattering coefficients extracted from a RADARSAT image (C-HH) that was acquired at same time with the soil sampling at the Jilantai Salt Lake area. We discovered that the correlation coefficient between /spl sigma//sup 0/ extracted from the RADARSAT image and /spl epsiv/" measured in the soil sample is 0.70, whereas the correlation coefficient of /spl sigma//sup 0/ and /spl epsiv/' is 0.27. The correlation coefficient between /spl sigma//sup 0/ and the soil sample salinity is 0.69. This suggests that soil salinity has a significant contribution to the backscattering coefficient /spl sigma//sup 0/ recorded in a synthetic aperture radar (SAR) image. Consequently, a SAR image can be a useful tool for monitoring soil salinity.

Using the Vector Microwave Network Analyzer in broad band (from 0.2 to 18 GHz), a controlled laboratory experiment was conducted on artificially prepared wet saline soil samples. The complex dielectric constants of soil samples in a wide range of moisture and salinity contents were measured and the relationship to the moisture and salinity of the soils were analyzed. The experiment results shows that the imaginary part of the dielectric constant of wet soil is more sensitive to the salinity of soils at low frequency range (f<5 GHz). It indicates that L band or P band SAR can be more effective in soil salinity detection. Secondly, 41 soil samples were collected along a profile from a dried salt lake in late April of 2001. Their moisture and salinity vary along the profile differently. The Vector Microwave Network Analyzer measured the complex dielectric constants of the collected soil samples in laboratory. C band, HH polarization RADARSAT data were simultaneously acquired on April 23, 2001. The backscatter intensity taken from RADARSAT image correlates with the complex dielectric constant. The correlation coefficient between the backscatter intensity and the real part of the complex dielectric constant is 0.23, and the imagine part of the complex dielectric constant is 0.66. This result shows that SAR will contribute to salinity monitoring program.

This paper makes three contributions. It clarifies the definition of edge preservation in SAR images. The definition of edge preservation is important for edge preservation evaluation of digital speckle filters. Only if the definition of edge preservation is reasonable, can the edge preservation evaluation be successfully performed. Second, an algorithm to evaluate edge preservation of digital speckle filters is proposed. Third, we analyze the reason why the enhanced Lee, enhanced Frost, gamma, and Kuan filters cannot effectively preserve edges. The main contribution in this paper is that we clarified the difference between edge preservation and edge point preservation. It is important to develop new methods to reduce speckle in SAR images, which can successfully preserve edges in smoothed images.

An effective algorithm using the multiscale technique for detecting edges in synthetic aperture radar (SAR) images is presented utilizing the empirical mode decomposition (EMD). Since EMD is a one-dimensional data processing method, EMD is employed to image in two directions: horizontally (0/spl deg/) and vertically (90/spl deg/) to obtain two directionally smoothed images at different scales. In this way, different scales of image data were obtained. In order to obtain edge points of single pixel width, the grad of different image scales is computed. The edge map is extracted from different image scales based on a uniform space condition. Application of this method to SAR images has shown that the effectiveness of the method is quite satisfactory.

Page's (1954) test is optimal for detecting a permanent change in distribution, in the sense that it minimizes the worst case average delay to detection given an average distance between false alarms. When used to detect transient signals, however, it in fact becomes the generalized likelihood ratio test (GLRT). Since a GLRT is in almost all cases ad hoc, Page's test used as such cannot be said to be optimal in any explicit sense. This article discusses the development of the min-max test, via the new ideas of Baygun and Hero (see ibid., vol.41, p.688-703, 1995) for the detection of a transient.

Page's (1954) test is optimal for detecting a permanent change in the distribution, in the sense that it minimizes the average delay to detection given an average distance between false alarms. When used to detect transient signals, however, it in fact becomes the generalized likelihood ratio test (GLRT). Since a GLRT is in almost all cases ad hoc, Page's test used as such cannot be said to be optimal in any explicit sense. The subject of this paper is the development of the min-max test for the detection of a transient.

The performance of Page's (1954) test for the detection of a permanent change in distribution is reasonably well understood. However, there are few parallel results on its application to the detection of a temporary (i.e., transient) change, and this is the paper's subject. Specifically, a lower bound on detectability is developed using a quantization approach; and a pair of approximations are presented, one based on a Brownian motion analogy, which yields an upper bound in the Gaussian case, and the other again on quantisation. The correspondence between these and simulation appears good in both Gaussian and non-Gaussian cases with heavier tail probability.