Segmentation of synthetic aperture radar image using multiscale information measure-based spectral clustering

A multiscale information measure (MIM), calculable from per-pixel wavelet coefficients, but relying on global statistics of synthetic aperture radar (SAR) image, is proposed. It fully exploits the variations in speckle pattern when the image resolution varies from course to fine, thus it can capture the intrinsic texture of the scene backscatter and the texture due to speckle simultaneously. Graph spectral segmentation methods based on MIM and the usual similarity measure are carried out on two real SAR images. Experimental results show that MIM can characterize texture information of SAR image more effectively than the commonly used similarity measure.