|Budget Amount *help
¥2,400,000 (Direct Cost : ¥2,400,000)
Fiscal Year 1998 : ¥600,000 (Direct Cost : ¥600,000)
Fiscal Year 1997 : ¥1,800,000 (Direct Cost : ¥1,800,000)
Understanding of the statistical properties of synthetic aperture radar (SAR) images is important for many applications. The intensity of SAR images fluctuates spatially by the multiplicative interaction of speckle and texture. Texture originates from the scene-dependent spatial variability, and contains useful information about targets. In this work, for a texture feature fractal dimension was considered.
The fractional Brownian motion (fBm) is known to be a typical model for stochastically fractal processes. Recently, P.Flandrin introduced the method that estimates the Hurst parameter of a one-dimensional (1-D) fBm, which is directly related to the fractal dimension, by wavelet-based multiresolution analysis. We extended the method to the 2-D case, and showed that the Hurst parameter of a 2-D image can be estimated in the same procedure as the l-D case.
Then the method was applied to actual SAR images. The results revealed that the SAR images were well modeled by the mm's, and the fractal dimensions of the SAR images were different in frequency bands and polarization channels. Since the fractal dimension is considered to feature the surface roughness intuitively, the results indicate that the manner of describing the roughness differs with frequency and polarization.
Furthermore we studied the relation between texture and system resolution, In these years, the resolution of SAR systems has been enhanced remarkably. The statistical model of high resolution data, however, is not fully established. So we examined the validity of the product model of texture and speckle for high resolution images before measuring their fractal dimension. In this analysis, the full polarimetric datasets were used, so that the relation between texture and polarization was investigated.