Two adaptive filters for speckle reduction in SAR images by using the variance ratio

Abstract This paper presents some experimental results on the properties of image speckle along with two adaptive filters that are useful for speckle reduction. An investigation of available SIR-B digital image data over Australia shows that speckle is non-while Gaussian noise and fits a multiplicative noise model in which the noise, uncorrelated with signal, has a mean of 1 and a constant standard deviation. The non-uniform spectrum can be represented by an empirical formula. Based on these results, two adaptive filters have been designed, a moving average filter and a combined moving median filter. In both of these, variance ratios are used to govern the filter parameters. The filters have been applied to some SIR-B image segments, showing good results in both smoothing speckle and maintaining edges.

[1]  Patrenahalli M. Narendra,et al.  A Separable Median Filter for Image Noise Smoothing , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  P. Danielsson Getting the Median Faster , 1981 .

[3]  T R Crimmins,et al.  Geometric filter for speckle reduction. , 1985, Applied optics.

[4]  Thomas S. Huang,et al.  A fast two-dimensional median filtering algorithm , 1979 .

[5]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

[6]  Thomas S. Huang,et al.  Image enhancement using the median and the interquartile distance , 1984, Comput. Vis. Graph. Image Process..

[7]  J. Goodman Some fundamental properties of speckle , 1976 .

[8]  B. R. Hunt,et al.  The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer , 1973, IEEE Transactions on Computers.

[9]  B. Frieden A new restoring algorithm for the preferential enhancement of edge gradients , 1976 .

[10]  Robin N. Strickland Estimation of local statistics for digital processing of nonstationary images , 1985, IEEE Trans. Acoust. Speech Signal Process..

[11]  Gary Mastin,et al.  Adaptive filters for digital image noise smoothing: An evaluation , 1985, Comput. Vis. Graph. Image Process..

[12]  H. Saunders Literature Review : RANDOM DATA: ANALYSIS AND MEASUREMENT PROCEDURES J. S. Bendat and A.G. Piersol Wiley-Interscience, New York, N. Y. (1971) , 1974 .

[13]  A.K. Jain,et al.  Advances in mathematical models for image processing , 1981, Proceedings of the IEEE.

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

[15]  G. Wise,et al.  A theoretical analysis of the properties of median filters , 1981 .

[16]  M. H. van Emden Increasing the efficiency of quicksort , 1970, CACM.

[17]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[18]  Jong-Sen Lee,et al.  Refined filtering of image noise using local statistics , 1981 .

[19]  G. S. Robinson Edge detection by compass gradient masks , 1977 .

[20]  N. Gallagher,et al.  Two-dimensional root structures and convergence properties of the separable median filter , 1983 .

[21]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[22]  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.

[23]  Robert A. Shuchman,et al.  Textural Analysis And Real-Time Classification of Sea-Ice Types Using Digital SAR Data , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Jong-Sen Lee,et al.  Digital image smoothing and the sigma filter , 1983, Comput. Vis. Graph. Image Process..

[25]  Thomas S. Huang,et al.  A generalization of median filtering using linear combinations of order statistics , 1983 .