Localization is the most important basic information for unmanned aerial vehicles (UAV) during their missions. Currently, most UAVs use GNSS to calculate their own position. However, when faced with complex electromagnetic interference situations or multipath effects within cities, GNSS signals can be interfered with, resulting in reduced positioning accuracy or even complete unavailability. To avoid this situation, this paper proposes an autonomous UAV localization method for low-altitude urban scenarios based on POI and store signage text matching (LPS) in UAV images. The text information of the store signage is first extracted from the UAV images and then matched with the name of the POI data. Finally, the scene location of the UAV images is determined using multiple POIs jointly. Multiple corner points of the store signage in a single image are used as control points to the UAV position. As verified by real flight data, our method can achieve stable UAV autonomous localization with a positioning error of around 13 m without knowing the exact initial position of the UAV at take-off. The positioning effect is better than that of ORB-SLAM2 in long-distance flight, and the positioning error is not affected by text recognition accuracy and does not accumulate with flight time and distance. Combined with an inertial navigation system, it may be able to maintain high-accuracy positioning for UAVs for a long time and can be used as an alternative to GNSS in ultra-low-altitude urban environments.

The occlusion of cloud layers affects the accurate acquisition of ground object information and causes a large amount of useless remote-sensing data transmission and processing, wasting storage, as well as computing resources. Therefore, in this paper, we designed a lightweight composite neural network model to calculate the cloud amount in high-resolution visible remote-sensing images by training the model using thumbnail images and browsing images in remote-sensing images. The training samples were established using paired thumbnail images and browsing images, and the cloud-amount calculation model was obtained by training a proposed composite neural network. The strategy used the thumbnail images for preliminary judgment and the browsing images for accurate calculation, and this combination can quickly determine the cloud amount. The multi-scale confidence fusion module and bag-of-words loss function were redesigned to achieve fast and accurate calculation of cloud-amount data from remote-sensing images. This effectively alleviates the problem of low cloud-amount calculation, thin clouds not being counted as clouds, and that of ice and clouds being confused as in existing methods. Furthermore, a complete dataset of cloud-amount calculation for remote-sensing images, CTI_RSCloud, was constructed for training and testing. The experimental results show that, with less than 13 MB of parameters, the proposed lightweight network model greatly improves the timeliness of cloud-amount calculation, with a runtime is in the millisecond range. In addition, the calculation accuracy is better than the classic lightweight networks and backbone networks of the best cloud-detection models.

In the original publication [...]

With the increasing demand for earth observation in various fields, remote satellites play an important role in ground information assurance. Apparently, the effective scheduling and utilization of multi-satellite resources determine the quality and efficiency of information acquisition. In this paper, focusing on the problem of centralized multi-satellite scheduling, we establish a mathematical model of satellite scheduling with complex constraints of load and platform operation. We also propose a real-coding Population Incremental Based Learning (PBIL) algorithm to solve the multi-satellite scheduling problem. The real-coding format can greatly shorten the coding length compared to the traditional PBIL algorithm with binary coding so that the computational efficiency is improved. Additionally, we design a value probability matrix, correction coefficient and mutation operator to guide better evolution and avoid early convergence. Finally, we take some numerical examples to verify the real-coding PBIL algorithm for multi-satellite scheduling. The performance of the algorithm is analyzed by comparing it with binary-coding PBIL and the Genetic Algorithm (GA). Additionally, the influence of key parameters on algorithm performance, such as probability correction coefficient, is also analyzed.

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