The solar-blind ultraviolet (UV) and visible (VIS) imaging system provides a valuable tool for search and rescue missions. However, due to atmospheric scattering and absorption effects, the UV images are significantly degraded, even missing the target in some frames. A framework based on a weighted mask, with three schemes suitable for various imaging conditions is proposed. Compared with traditional methods, this framework not only preserves low-intensity target regions but also highlights and tracks any suspicious target. Scheme 1 enhances the signal-to-noise ratio (SNR) by computing the accumulating weights of sequential frames, supporting temporal and average weighting means. The temporal weighting serves as a traditional recursive temporal filtering method, which has an effect similar to that of average weighting. Scheme 2 mitigates small platform drifts by introducing a Kalman filter. Scheme 3 mitigates large platform perturbations by eliminating interference from a moving background, which is achieved by determining the warping relationship from adjacent VIS frames. The experiments are designed to cover as many situations as possible, including low-SNR imaging on a static platform, high-SNR imaging on a flat-flying small drone, and strong/weak complex target imaging on a hovering platform. The experiments assess the proposed methods and validate their predicted performance.
This paper presents a genetic algorithm for bundle adjustment in aerial panoramic stitching. Compared with the conventional LM (Levenberg-Marquardt) algorithm for bundle adjustment, the proposed bundle adjustment combining the genetic algorithm optimization eliminates the possibility of sticking into the local minimum, and not requires the initial estimation of desired parameters, naturally avoiding the associated steps, that includes the normalization of matches, the computation of homography transformation, the calculations of rotation transformation and the focal length. Since the proposed bundle adjustment is composed of the directional vectors of matches, taking the advantages of genetic algorithm (GA), the Jacobian matrix and the normalization of residual error are not involved in the searching process. The experiment verifies that the proposed bundle adjustment based on the genetic algorithm can yield the global solution even in the unstable aerial imaging condition.