Analysis of speckle noise contribution on wavelet decomposition of SAR images

This paper describes the use of the wavelet transform for multiscale texture analysis. One of the basic problems is that texture measures have to adapt to the peculiarity of radar images that contain multiplicative speckle noise. In this paper, the focus is on the effect of speckle on the wavelet transform. The effect is first assessed analytically. It is shown that the wavelet coefficients are modulated by the multiplicative character of the speckle in a manner that is proportional to the target mean backscattering coefficient. The effect of speckle correlation is also demonstrated. Wavelet decomposition is then applied to a simulated radar image generated by a Monte Carlo approach and based on a statistical model. Modeling shows that the correlation properties of speckle have an effect up to a scale that corresponds to its granular size. The results also show that the main contribution to the wavelet transform for an homogeneous area is the first-order statistical distribution of speckle, which remains important even at large scales. The results are then compared to a ERS-1 synthetic aperture radar (SAR) image of a primary tropical forest region