The method of collecting aerial images or videos by unmanned aerial vehicles (UAVs) for object search has the advantages of high flexibility and low cost, and has been widely used in various fields, such as pipeline inspection, disaster rescue, and forest fire prevention. However, in the case of object search in a wide area, the scanning efficiency and real-time performance of UAV are often difficult to satisfy at the same time, which may lead to missing the best time to perform the task. In this paper, we design a wide-area and real-time object search system of UAV based on deep learning for this problem. The system first solves the problem of area scanning efficiency by controlling the high-resolution camera in order to collect aerial images with a large field of view. For real-time requirements, we adopted three strategies to accelerate the system, as follows: design a parallel system, simplify the object detection algorithm, and use TensorRT on the edge device to optimize the object detection model. We selected the NVIDIA Jetson AGX Xavier edge device as the central processor and verified the feasibility and practicability of the system through the actual application of suspicious vehicle search in the grazing area of the prairie. Experiments have proved that the parallel design of the system can effectively meet the real-time requirements. For the most time-consuming image object detection link, with a slight loss of precision, most algorithms can reach the 400% inference speed of the benchmark in total, after algorithm simplification, and corresponding model’s deployment by TensorRT.

A super-resolution (SR) reconstruction of remote sensing images is becoming a highly active area of research. With increasing upscaling factors, richer and more abundant details can progressively be obtained. However, in comparison with natural images, the complex spatial distribution of remote sensing data increases the difficulty in its reconstruction. Furthermore, most SR reconstruction methods suffer from low feature information utilization and equal processing of all spatial regions of an image. To improve the performance of SR reconstruction of remote sensing images, this paper proposes a deep convolutional neural network (DCNN)-based approach, named the deep residual dual-attention network (DRDAN), which achieves the fusion of global and local information. Specifically, we have developed a residual dual-attention block (RDAB) as a building block in DRDAN. In the RDAB, we firstly use the local multi-level fusion module to fully extract and deeply fuse the features of the different convolution layers. This module can facilitate the flow of information in the network. After this, a dual-attention mechanism (DAM), which includes both a channel attention mechanism and a spatial attention mechanism, enables the network to adaptively allocate more attention to regions carrying high-frequency information. Extensive experiments indicate that the DRDAN outperforms other comparable DCNN-based approaches in both objective evaluation indexes and subjective visual quality.

Image super-resolution (SR) technology aims to recover high-resolution images from low-resolution originals, and it is of great significance for the high-quality interpretation of remote sensing images. However, most present SR-reconstruction approaches suffer from network training difficulties and the challenge of increasing computational complexity with increasing numbers of network layers. This indicates that these approaches are not suitable for application scenarios with limited computing resources. Furthermore, the complex spatial distributions and rich details of remote sensing images increase the difficulty of their reconstruction. In this paper, we propose the pyramid information distillation attention network (PIDAN) to solve these issues. Specifically, we propose the pyramid information distillation attention block (PIDAB), which has been developed as a building block in the PIDAN. The key components of the PIDAB are the pyramid information distillation (PID) module and the hybrid attention mechanism (HAM) module. Firstly, the PID module uses feature distillation with parallel multi-receptive field convolutions to extract short- and long-path feature information, which allows the network to obtain more non-redundant image features. Then, the HAM module enhances the sensitivity of the network to high-frequency image information. Extensive validation experiments show that when compared with other advanced CNN-based approaches, the PIDAN achieves a better balance between image SR performance and model size.

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Walker constellation is the most effective constellation for global coverage and is often used for earth observation, navigation and internet communication. A scenario that several faulted satellites in a Walker constellation lead to its performance degradation can be quickly repaired to a certain extent by reconstructing the on-orbit satellite configuration. Different from the classical strategies such as adjusting the phase of the adjacent satellites, uniform the on-satellites' phase and adjusting the plane of the adjacent satellites, this paper proposes a bionic reconstruction method which uses the elastic mechanics method of thin film plate to generate the satellite maneuver strategy in the constellation reconstruction process, minimizes the performance degradation between the repaired configuration and the previous configuration. Thus, the maneuver strategy of each satellite can be calculated in reversely. The simulation example shows that the maneuver strategy by the bionic reconstruction method is more harmonious and natural than the classical strategies.