Feature - Enhanced Synthetic Aperture Radar Imaging

Remotely sensed images have already attained an important role in a wide spectrum of tasks ranging from weather forecasting to battlefield reconnaissance. One of the most promising remote sensing technologies is the imaging radar, known as synthetic aperture radar (SAR). SAR overcomes the nighttime limitations of optical cameras, and the cloud-cover limitations of both optical and infrared imagers. In current systems, techniques such as the polar format algorithm are used to form images from the collected SAR data. These images are then interpreted by human observers. However, the anticipated high data rates and the time critical nature of emerging SAR tasks motivate the use of automated processing or decision-making techniques in information extraction from the reconstructed images. The success of such automated decision-making (e.g. object recognition) depends on how well SAR images exhibit certain features of the underlying scene. Unfortunately, current SAR image formation techniques have no explicit means to highlight features useful for automatic interpretation. Furthermore, these techniques are usually not robust to reduced quality or quantity of data. We have developed a mathematical foundation and associated algorithms for feature-enhanced SAR imaging to address such challenges. Our framework is based on a regularized reconstruction of the scattering field which combines a tomographic