Pose Error Reduction for Focus Enhancement in Thermal Synthetic Aperture Visualization
This work is significant for researchers and practitioners in remote sensing and aerial imaging who need to visualize objects occluded by foliage, by improving the accuracy of the underlying pose estimation.
This paper addresses the problem of precise drone pose measurements for airborne optical sectioning, which is used to visualize artifacts hidden by forests. They propose a new approach that treats pose estimation as a focusing problem, leading to improved quality of synthetic integral images.
Airborne optical sectioning, an effective aerial synthetic aperture imaging technique for revealing artifacts occluded by forests, requires precise measurements of drone poses. In this article we present a new approach for reducing pose estimation errors beyond the possibilities of conventional Perspective-n-Point solutions by considering the underlying optimization as a focusing problem. We present an efficient image integration technique, which also reduces the parameter search space to achieve realistic processing times, and improves the quality of resulting synthetic integral images.