SAR Image Generation with Semantic-Statistical Convolution

SAR image due to its nature of coherent imaging manifests both deterministic semantic information and speckle-like statistical textures. It is necessary to have a general representation scheme of the semantic-statistical two-layer hierarchy of SAR image so that semantic and textural information can be separated. Inspired by the correlated clutter simulation method proposed by Bustos et al [1]–[2], this paper studies a semantic-statistical convolution scheme to generate a SAR image from a semantic map. For each terrain type, we estimate the intensity distribution and correlated texture model and then generate textures with correlated clutter. The method is tested on actual SAR images of E-SAR data including urban and forest areas and Flevoland AirSAR data with 15 terrains.