Cross-band Inverse Synthetic Aperture Radar (ISAR) image fusion

Research on image fusion is making rapid progress recently, because multiple looks of the same target from different aspects will increase the available knowledge and allow more useful target information to be extracted. Studying on the physical principle of constructing radar images, especially Inverse Synthetic Aperture Radar (ISAR) images, make the fusion from multiple individual images generated by radars at multiple locations becomes possible. However, it is a challenge for image fusion if the source images are of different geometric resolutions, which are determined by radar system parameters, for example, the bandwidth of transmitted signals. This paper analyzes the influences caused by the different image resolutions, modifies the data fusion method proposed by previous research, and applies the modified method to an actually measured database. The performance of the modified image fusion algorithm is evaluated by the Image Attribute Rating (IAR) curves. The results show that the data collected by radars working at X-band and Ka-band can be fused successfully, and the information contained in the signals at these two frequency bands are complementary to each other. Therefore, the fusion improves target feature detection and thereby enhances target recognition.