Multiscale segmentation and anomaly enhancement of SAR imagery
We present an efficient multiscale approach to the segmentation of natural clutter, specifically grass and forest, in synthetic aperture imagery (SAR) and to the enhancement of anomalous image regions therein. The methods we propose exploit the coherent nature of SAR sensors. In particular, they characterize the scale-to-scale statistical differences in imagery of various terrain categories due to radar speckle. To achieve this, we employ a recently introduced class of multiscale stochastic processes that provide a powerful framework for describing random processes and fields that evolve in scale. We build models representative of each relevant category of terrain and use them to direct subsequent decisions on pixel classification, segmentation, and anomaly presence.