Classification of Objects in SAR Images Using Scaling Features.
Uma Ranjan, Akash Narayana · Indian Conference on Computer Vision, Graphics and Image Processing · 2002
Synthetic Aperture Radar (SAR) is an important technique used for imaging of objects. Its strength lies in the fact that it is the only successful all-weather imaging system. However, SAR images suffer from clutter and speckle, and much research has been devoted to developing a pre-processor which can eliminate these. In this paper, we show that classification based on scaling information is naturally invariant to speckle and clutter. The methodology makes use of two kinds of scaling information in images Holder exponents and wavelet transform. It has been shown that these two features correspond to two different multiscale formalisms and essentially capture different kinds of behaviour. When used in conjunction with each other, they yield accurate classification on the MSTAR public domain database images.