Aiming at the intrinsic calibration of infrared cameras with certain atmospheric absorption bands, this paper proposes intrinsic-parameter correction methods based on the angular invariance of the infrared-star calibration points. The four cases of inner product cosine invariant and outer product sine invariant under the image model with or without distortion are compared and analyzed. According to the experimental results, the outer product sine invariant has higher correction accuracy due to the higher linearity for small angles, while the image model without distortion is more sensitive to the noise of star-centroid extraction, and the intrinsic calibration error is large. In addition, the experiment also proved that the noise of the star-centroid extraction should be controlled within half a pixel as much as possible; otherwise the accuracy of the intrinsic calibration may be reduced when the distortion in the field of view is severe. Experiments show that the sine-invariant correction algorithm under the distortion model is very suitable for high-sensitivity infrared camera intrinsic calibration using star points as a large number of control points.
For the high-sensitivity cameras of a super-large star catalog, the conventional effective star identification methods for star sensors will face huge storage and calculations that current computers cannot afford. This paper presents a two-stage full-sky star identification method. 3~4 prominent stars are firstly quickly identified from a simplified star catalog, to determine the view direction. Then, three different strategies are adopted to recognize other remaining stars in the field of view: one strategy is to automatically load the K-vector table of the corresponding sky zone; one strategy is to temporarily generate a K-vector table from the candidate star set, and then remaining stars are identified according to the angular distance from the prominent stars; the third strategy is to obtain the image coordinates of the candidate star set, the proximity position constraint is considered while constraining the angular distance from the prominent stars. Experiments show that the speed of the third strategy is increased by about 20% and maintains a higher recognition rate (F1 is about 0.92). This two-stage recognition method ingeniously resolves the huge amount of calculation caused by the super-large star catalog, and can identify enough stars (ten thousand stars) in a single frame, and provides sufficient control points for the subsequent intrinsic calibration.
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.
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