Fusion of multiple-look synthetic aperture radar images at data and image levels

Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) have proven capabilities for non-cooperative target recognition (NCTR) applications. Multiple looks of the same target (at different aspect angles, frequencies, etc.) can be exploited to enhance target recognition by fusing the information from each look. Such fusion can be performed at the raw data level or at the processed image level depending on what is available. At the data level, physics based image fusion techniques can be developed by processing the raw data collected from multiple inverse synthetic aperture radar (ISAR) sensors, even if these individual images are at different resolutions. The technique maps multiple data sets collected by multiple radars with different system parameters on to the same spatial-frequency space. The composite image can be reconstructed using the inverse 2-D Fourier Transform over the separated multiple integration areas. An algorithm called the Matrix Fourier Transform (MFT) is proposed to realize such a complicated integral. At the image level, a persistence framework can be used to enhance target features in large, aspect-varying datasets. The model focuses on cases containing rich aspect data from a single depression angle. The goal is to replace the data's intrinsic viewing geometry dependencies with target-specific dependencies. Both direct mapping functions and cost functions are presented for data transformation. An intensity-only mapping function is realized to illustrate the persistence model in terms of a canonical example, visualization, and classification.