When the targets are distributed irregularly, it is of great significance to quickly acquire color images with a wide field by agile satellite. Attributed to the changing of the three-axis attitude and Earth’s rotation, there are misalignment and distortion during the processing of imaging by agile satellite. It makes the fusion of interband images slower and the stitching of interdetector images poorer. In this article, a rigorous velocity vector field (VVF) model is established through tracing rays between the image and the object. It calculates the misalignment among the images of bands in each detector to align and fuse multispectral images. Based on the reference image selected by VVF, the fused images are stitched with the method of leveraging line-point consistency. Finally, both field and in-orbit experiments are conducted. The time of calculating misalignment in image fusion with VVF is 13.19% of the minimum time of the traditional registration algorithm. In image stitching, our RMSE values are 1.15, which is the smallest among the RMSE values of SIFT, SURF, and SPW. In preserving linear structure, compared with SPW, the values of ${E_{\text {dis}}}$ , ${E_{\text {err}}}$ , and ${E_{\text {cross}}}$ , respectively, reduce 60%, 50%, and 50%. The results show that images of the agile satellite can be fast fused and precisely stitched using VVF while correcting distortion. The compensation strategy of the VVF provides a theoretical basis for the agile satellite in image fusion and stitching.

Reference-based super-resolution (RefSR) has achieved remarkable progress and shows promising potential applications in the field of remote sensing. However, previous studies heavily rely on existing and high-resolution reference image (Ref), which is hard to obtain in remote sensing practice. To address this issue, a novel structure based on a zoom camera structure (ZCS) together with a novel RefSR network, namely AEFormer, is proposed. The proposed ZCS provides a more accessible way to obtain valid Ref than traditional fixed-length camera imaging or external datasets. The physics-enabled network, AEFormer, is proposed to super-resolve low-resolution images (LR). With reasonably aligned and enhanced attention, AEFormer alleviates the misalignment problem, which is challenging yet common in RefSR tasks. Herein, it contributes to maximizing the utilization of spatial information across the whole image and better fusion between Ref and LR. Extensive experimental results on benchmark dataset RRSSRD and real-world prototype data both verify the effectiveness of the proposed method. Hopefully, ZCS and AEFormer can enlighten a new model for future remote sensing imagery super-resolution.

The rolling shutter effect decreases the accuracy of the attitude measurement of star trackers when they work in rolling shutter exposure mode, especially under dynamic conditions. To solve this problem, a rolling shutter effect correction method based on particle swarm optimization is proposed. Firstly, a collinear reverse installation method between the star tracker and the satellite is proposed, which simplifies the relationship between the velocity of the star centroid and the star tracker angular velocity. Next, the centroid error model is obtained by the star centroid velocity. Based on the centroid error model and angular distance invariance, the loss function of the centroid error is proposed. Then, the particle swarm optimization algorithm is used to determine the star tracker angular velocity by minimizing the loss function. Finally, the simulation and experiments are carried out to verify the proposed method. The experimental results show that the convergence times of the algorithm are less than 50 and the root mean square error (RMSE) of the angular velocity is better than 0.02°/s when the angular velocity of the star tracker is no more than 5°/s.