Characterization of spatial statistics of distributed targets in SAR data

Abstract A method for the analysis of spatial statistics in multifrequency polarimetric Synthetic Aperture Radar (SAR) data is presented. The objective is to extract the intrinsic variability of the target by removing the variability from other sources. Three sources which contribute to the spatial variability in the returned power from a distributed target are modelled, they are (1) image speckle, (2) system noise, and (3) the intrinsic spatial variability of the target or texture. Speckle and system noise are modelled based on an understanding of the physics of the SAR imaging and processing systems. Texture is modelled as a random variable which modulates the mean returned power from a distributed target. An image model which accounts for all three sources of variability is presented. The presence of texture is shown to increase the image variance-to-mean square ratio and to introduce deviations of the image autocovariance function from the expected SAR system response. Two textural parameters, the sta...

[1]  E. Jakeman On the statistics of K-distributed noise , 1980 .

[2]  F. Ulaby,et al.  Textural Infornation in SAR Images , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[3]  B. C. Barber,et al.  Theory of Digital Imaging from Orbital Synthetic Aperture Radar , 1983 .

[4]  R. Raney Transfer Functions for Partially Coherent SAR Systems , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[5]  H. Laur Analyse d'images radar en teledetection : discriminateurs radiometriques et texturaux , 1989 .

[6]  Jong-Sen Lee,et al.  Speckle analysis and smoothing of synthetic aperture radar images , 1981 .

[7]  Victor S. Frost,et al.  A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Barbara A. Burns,et al.  Characterization of sea ice types using synthetic aperture radar , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[10]  J. Goodman Statistical Properties of Laser Speckle Patterns , 1963 .

[11]  David Lyzenga,et al.  On the Estimation of Wave Slope-and Height-Varnance Spectra from SAR Imagery , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[12]  C. Oliver The interpretation and simulation of clutter textures in coherent images , 1986 .

[13]  Jerry S. Zelenka,et al.  Comparison of continuous and discrete mixed-integrator processors , 1976 .

[14]  Rama Chellappa,et al.  Segmentation of synthetic-aperture-radar complex data , 1991 .

[15]  E. Jakeman,et al.  A model for non-Rayleigh sea echo , 1976 .

[16]  Robert C. Beal,et al.  Large‐and small‐scale spatial evolution of digitally processed ocean wave spectra from SEASAT synthetic aperture radar , 1983 .

[17]  M. Kendall,et al.  The advanced theory of statistics , 1945 .

[18]  J. Kong,et al.  K-Distribution and Polarimetric Terrain Radar Clutter , 1990, Progress In Electromagnetics Research.

[19]  R. A. Cordey,et al.  Complex SAR imagery and speckle filtering for wave imaging , 1989 .

[20]  P. W. Vachon,et al.  Estimation of the SAR system transfer function through processor defocus , 1989 .