High-Resolution SAR Image Classification via Multiscale Local Fisher Patterns

With the increased number of synthetic aperture radar (SAR) systems that have been launched in recent years, many high-quality images have been produced for precise earth observation. The number of available images requires new feature extraction techniques to capture enough discriminant information from high-resolution SAR images. To effectively characterize different land covers, a multiscale local Fisher pattern (MLFP) is proposed to extract both statistical and spatial properties in this work. First, the Fisher vector (FV) of each pixel is calculated after the statistical distribution of high-resolution SAR intensities is modeled via a Gamma mixture model. Then, the generative and discriminant information implied in the FV is exploited after the extraction of the geometrical information between neighboring pixels within different scales. Two experiments are conducted to demonstrate the performance of the proposed MLFP, which are high-resolution SAR image land-cover classification and SAR scene classification. Compared with some existing approaches, the proposed MLFP shows superior performance after the statistical and spatial structure information from different land covers is exploited.