Chair: David Munson, University of Illinois at Urbana Champaign (USA)
Hui Jiang, Chinese Academy of Sciences (PEOPLES REPUBLIC OF CHINA)
Chao-Huan Hou, Chinese Academy of Sciences (PEOPLES REPUBLIC OF CHINA)
An algorithm to process the Synthetic Aperture Radar (SAR) data in wavenumber domain,has been discussed in this paper. Generated from the seismic migration technique in the area of geophysics, this imaging method has been modified and developed to suit the fracture of SAR imaging. In this paper, after introducing the basic idea of original seismic migration, a scheme of SAR imaging is derived. The result shows that the new algorithm will improve the imaging quality without additional computational cost because the correction can be simplified to phase shifting and fused to pulse compression in azimuth direction.
S. Young, SUNY at Buffalo (USA)
N. Nasrabadi, SUNY at Buffalo (USA)
M. Soumekh, SUNY at Buffalo (USA)
This paper presents methods for detecting and identifying moving targets in a Synthetic Aperture Radar (SAR) scene. An analytical expression is derived for the coherent SAR signature of a target. SAR system model of a moving target is developed. These principles are then used to construct a SAR signal statistic (energy function) in a parameter space which is defined by the target's coordinates, speed, and coherent SAR signature. Stochastic gradient techniques are used to search for the maximum point of this energy function which is located at the desired target's parameters.
Matthew P. Pepin, Air Force Institute of Technology
Michael P. Clark, Air Force Institute of Technology
Jian Li, University of Florida (USA)
This paper examines the modeling of synthetic aperture radar (SAR) phase histories with 2-D damped exponential models of low order. The use of a low order model is warranted when the radar returns are attributable to a small number of point scatterers. In this paper we show that the fit of the widely used damped exponential model is highly dependent on the image scene. Specifically, current high resolution methods have limited applicability due to mismatch between the assumed model and observed data.
Daniele D. Guisto, University of Cagliari (ITALY)
Lilla Boroczky, Hungarian Academy of Sciences (HUNGARY)
Roberto Fioravanti, Hungarian Academy of Sciences (HUNGARY)
Stefano Fioravanti, Saclant Undersea Research Center (ITALY)
This paper presents a novel multiresolution wavelet-based algorithm for filtering SAR images in order to remove speckle noise. The basic idea is to apply to the wavelet coefficients a size-decreasing half-interpolated median filter. The size of the median filter is adapted to the noise energy reduction between the image pyramid levels and different filter shapes are used in each wavelet subband according to the dominant frequencies. Experimental results showed, that the proposed algorithm results in significant noise removal while the edges are preserved in the images.
Nikola S. Subotic, Environmental Research Institute of Michigan (USA)
Leslie M. Collins, Environmental Research Institute of Michigan (USA)
John D. Gorman, Environmental Research Institute of Michigan (USA)
Brian J. Thelen, Environmental Research Institute of Michigan (USA)
We demonstrate the utility of a multiresolution approach for target detection in SAR imagery. Man-made objects exhibit characteristic phase and amplitude fluctuations as the image resolution is varied, while natural terrain has a random signature. We construct a number of detection strategies: an optimal invariant multiresolution detector based on a derived multiresolution increments process; and a generalized likelihood ratio detector to differentiate between a first order autoregressive multiresolution increments process and white noise. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution.