Object detection in remote sensing images is challenging due to the dense distribution and arbitrary angle of the objects. It is a consensus that the oriented bounding box (OBB) is more suitable to represent the aerial objects. However, there are some extreme cases in regression-based OBB detection that make the regression target discontinuous, resulting in poor performance. In this paper, an analysis of the formats of OBB and the problems in its regression is presented, following with an exploration of transform localization from regression to keypoint estimation, which could be applied to avoid the problem of discontinuous regression target. Our novel method is called Object-wise Point-guided Localization Detector (OPLD). Continuously, a new prediction of center-point is introduced to refine the results, as the truncation problem caused by the cut graph. Lastly, in order to figure the problem of inconsistency between the localization quality and the classification score, both the endpoint scores and the classification score are adopted weighting as a result score. Experimental results are based on two widely used datasets, i.e., DOTA and HRSC2016. OPLD achieve 76.43% mAP and 78.35% mAP in oriented bounding boxes (OBB) and horizontal bounding boxes (HBB) tasks of DOTA-v1.0, which achieves state-of-the-art performance, respectively. Project page at https://github.com/yf19970118/OPLD-Pytorch.

Large-scale caption-labeled remote sensing image samples are expensive to acquire, and the training samples available in practical application scenarios are generally limited. Therefore, remote sensing image caption generation tasks will inevitably fall into the dilemma of few-shot, resulting in poor qualities of the generated text descriptions. In this study, we propose a self-learning method named SFRC for few-shot remote sensing image captioning. Without relying on additional labeled samples and external knowledge, SFRC improves the performance in few-shot scenarios by ameliorating the way and efficiency of the method of learning on limited data. We first train an encoder for semantic feature extraction using a supplemental modified BYOL self-supervised learning method on a small number of unlabeled remote sensing samples, where the unlabeled remote sensing samples are derived from caption-labeled samples. When training the model for caption generation in a small number of caption-labeled remote sensing samples, the self-ensemble yields a parameter-averaging teacher model based on the integration of intermediate morphologies of the model over a certain training time horizon. The self-distillation uses the self-ensemble-obtained teacher model to generate pseudo labels to guide the student model in the next generation to achieve better performance. Additionally, when optimizing the model by parameter back-propagation, we design a baseline incorporating self-critical self-ensemble to reduce the variance during gradient computation and weaken the effect of overfitting. In a range of experiments only using limited caption-labeled samples, the performance evaluation metric scores of SFRC exceed those of recent methods. We conduct percentage sampling few-shot experiments to test the performance of the SFRC method in few-shot remote sensing image captioning with fewer samples. We also conduct ablation experiments on key designs in SFRC. The results of the ablation experiments prove that these self-learning designs we generated for captioning in sparse remote sensing sample scenarios are indeed fruitful, and each design contributes to the performance of the SFRC method.

1. Conventional remote sensing image interpretation tasks obtain image-level semantic information by processing tags or key targets, while the image captioning (IC) is concerned with generating a s...

The traversal inspection of satellites in satellite constellations or geosynchronous orbits has been a focus of research. A large number of variable orbit requirements in the “single-to-single” mode severely affects the efficiency of inspections. To address this problem, this study investigated the problem of a single-impulse flyby co-orbiting two spacecraft and proposed a derivative-free numerical solution method that used the geometric relationship between the two intersections of the target and transfer orbits of the flyby problem in order to transform them into a nonlinear equation in a single variable for a given impulse time. The validity of the proposed method was verified using numerical examples. While the Lambert problem is one of the bases for solving the variable orbit problem, on-star intelligent control also raises the requirements for speed. To address this problem, this study also investigated the Lambert problem in a single-impulse flyby co-orbiting two spacecraft and determined the iterative initial value by constructing a quadratic interpolation equation between the inverse of the transfer time and the vertical component of the eccentric vector, the derivative-free quadratic interpolation cut-off method was proposed. Using 100,000 random tests showed that computational efficiency was improved by more than one order of magnitude compared with commonly used methods, with a calculation error of less than 10⁻⁶.

Metalenses have attracted much attention from researchers because they are thin, multifunctional and easy to integrate. However, due to the limitation of fabrication techniques and the influence of working wavelength, it is difficult to realize a large aperture. In this paper, we design and simulate polarization-insensitive, orthogonal linearly polarized and orthogonal circularly polarized synthetic aperture metalenses. We use a synthetic aperture to improve the cutoff frequency of the metalens and achieve an equivalent large-aperture metalens. We compare the modulation transfer function and focusing efficiency of synthetic aperture metalenses with different array structures. This work provides an effective solution to the needs of large-aperture metalenses, and metalenses with different polarizations can play an important role in polarization imaging.

The 6DOF coupled control for spatial proximity operation is the critical technique for the observer spacecraft to take the monitoring and spotting mission. In order to solve the 6DOF coupled effect issue in translation and rotation control for spacecraft, adapt the dual quaternion framework is elegant and integrated. However, adapting with this model, the estimation of the dual inertial matrix cannot be directly constructed. In this article, a direct method is devised to create the estimation of dual inertial matrix derivative by extending the dual quaternion to diagonal matrix and compositing with an antisymmetric identity matrix. An adaptive sliding mode control technique is developed based on this efficient parameter estimation algorithm that can compensate the uncertainties of the inertia parameters. The Lyapunov stability is proved that the controller is asymptotically stable. The convergence analysis is done accordingly to prove faster convergence. Finally, the comparison results are shown in the simulation that the better performance than some advanced sliding mode controllers. Overall convergence performance is compared with advanced adaptive fast terminal sliding mode control and adaptive high order super twist sliding mode control.