This paper presents an efficient, non-parametric and multiscale approach to segmentation of natural
clutter in synthetic aperture radar (SAR) imagery. The method we propose not only exploits the
coherent nature of SAR sensor, and takes advantage of the characteristic statistical difference in
imagery of different terrain types, but also does not require the distribution of pixels due to using
Bootstrap method. Firstly, we employ multiscale autoregressive (MAR) model for describing
random processes that evolve in scale. Secondly, the confidence interval of the parameters in MAR
model for each category of terrain of interest are calculated using Bootstrap technique. Then, for
each pixel, we generate a set of parameter estimation that characterizes the local evolution in scale.
The pixel is classified by relation between the parameters estimation and the confidence internal of
each type. Finally, test images are classified to demonstrate the method.agment-->{