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Applied Math Seminar
Adaptive Spatial-Temporal Image Processing Techniques and Applications to Remote Sensing
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In space-based infrared sensor systems, cluttered backgrounds are typically much more intense than the equivalent sensor noise or the targets being detected and tracked. Therefore, the development of efficient clutter removal and target preservation/enhancement algorithms is of crucial importance. We propose adaptive spatial-temporal image processing techniques that can be effectively used for background estimation and clutter filtering together with jitter compensation (scene stabilization). The results of simulations for space-based IR staring sensor systems (for various geometries, resolutions, illuminations and meteorological conditions) and processing of real data show that the developed algorithms allow for efficient clutter rejection in all tested situations. Proposed algorithms completely remove heavy clutter in the presence of substantial jitter and do not require expensive sub-pixel jitter stabilizers. In contrast, spatial-only filters and industry-standard temporal differencing methods can be used only for weak and relatively correlated clutter. |