SAR clutter analysis and its resolution dependence

Synthetic aperture radar (SAR) systems produce imagery that looks like a map of the region of interest. To detect small objects in SAR scenes a description of the surrounding area is usually required. This surrounding area of clutter, usually natural in origin, typically shows high pixel to pixel intensity fluctuations. Statistical noise models based on the K distribution have described well these clutter textural properties at low resolutions. This paper analyses whether statistical noise models, like the K distribution, provide a good description of natural clutter textures over a range of imaging resolutions. To obtain the most statistically significant result, a large high resolution SAR scene is analysed containing a variety of textures from ploughed fields, to orchards and towns. Statistical noise models require the region under analysis to be homogeneous, i.e. spatially invariant in its statistical properties. A method of automatically selecting homogeneous image patches from the whole scene is developed to allow a large number of independent measurements to be made of the image properties. For the purposes of testing, it is assumed that statistical noise models are appropriate and the degree to which the models fit the data is examined. Deviation from fitting is assessed as a function of distributional form and image resolution using the Kolmogorov Smirnov distribution test. Distribution parameters are found by maximising the likelihood of the data matching the distributions