Abstract: Methods and systems detect changes occurring over time between synthetic aperture sonar (SAS) images. A processor performs coarse navigational alignment, fine-scale co-registration and local co-registration between current image data and historical image data. Local co-registration includes obtaining correlation peaks for large neighborhood non-overlapping patches. Relative patch translations are estimated and parameterized into error vectors. Interpolation functions formed from the vectors re-map the current image onto the same grid as the historical image and the complex correlation coefficient between images is calculated. The resulting interferogram is decomposed into surge and sway functions used to define the argument of a phase function, which is multiplied by the current image to remove the effects of surge and sway on the interferogram. Based on the aforementioned computations, a canonical correlation analysis is performed to detect scene changes between the historical and new SAS images.

Abstract: Methods and systems detect changes occurring over time between synthetic aperture sonar (SAS) images. A processor performs coarse navigational alignment, fine-scale co-registration and local co-registration between current image data and historical image data. Local co-registration includes obtaining correlation peaks for large neighborhood non-overlapping patches. Relative patch translations are estimated and parameterized into error vectors. Interpolation functions formed from the vectors re-map the current image onto the same grid as the historical image and the complex correlation coefficient between images is calculated. The resulting interferogram is decomposed into surge and sway functions used to define the argument of a phase function, which is multiplied by the current image to remove the effects of surge and sway on the interferogram. Based on the aforementioned computations, a canonical correlation analysis is performed to detect scene changes between the historical and new SAS images.