Guangling Lai

Following the development of the unmanned aerial vehicle (UAV) industry, the number of UAVs has increased significantly, and higher expectations regarding the effect of spatial path planning have been expressed. Traditional coordinate-based conflict detection and path planning methods of UAVs are highly complex and have low computational efficiency. While the conflict detection method based on the grid system can improve efficiency, the rigid grid structure of the existing local grid system or discrete global grid system (DGGS) is insufficient. Since the rigid structure only encodes grid cells, multiple or different size grids are required to identify UAVs in motion, further complicating path planning. The main contributions of this paper are as follows. 1) We proposed a non-rigid hierarchical discrete grid structure and coding method for spatial 3D grids and grid data model. This method can accomplish the aggregation of any adjacent eight grids to form multiple non-rigid structures, making the original fixed relationship between parent and child grids more flexible. 2) The non-rigid grid structure optimizes the identification ability of grid vertices, grid edges, and grid faces. It has 8 times the identification ability of the rigid grid structure. We exploited this structure for UAV identification with the same size grid at any position of the grid structure. 3) Based on the non-rigid grid structure, an improved UAV conflict detection method and path planning method are designed. Experimental results indicate that, on an average, the efficiency of the improved conflict detection is an order of magnitude higher than that of the traditional computational detection method. The improved path planning method reduces the path length by an average of 11% compared to the single-grid planning method, and is 3.85 times more efficient than the multi-grid buffer planning method.

The lack of data samples has become a bottleneck for the performance improvement of bridge detection model training via transfer learning. This article proposes a deep learning bridge detection algorithm aided by a traditional bridge detection method to alleviate this problem. First, we explore the theoretical constraints on the integration of prior-information feature maps extracted via the traditional unsupervised method. Second, we design a prior-information auxiliary module (PAM) to receive and inject prior information. Finally, we introduce this module into different kinds of object detection networks for testing. The experimental results show that prior masks generated through multiextremal segmentation can assist object detectors in many respects. In addition, the PAM can use a few prior-information feature maps to enhance the performance of four representative object detection models on the bridge recognition dataset from the 2020 Gaofen challenge on automated high-resolution earth observation image interpretation. Our method preserves the validity of end-to-end training, making the traditional method more valuable in the artificial intelligence context.

Nominal grid data of geostationary remote sensing satellites are fundamental for generating the subsequent products. It can be obtained by normalized projection models, mainly based on the imaging mode. However, there are only definitions and primary descriptive equations for the normalized geostationary projection (NGP) model in the existing literature, while the corresponding imaging mode and the physical interpretation are missing, thus hindering the understanding of the produced nominal grid dataset as well as the subsequent products based on the grid. This paper first derived the imaging mode for NGP based on the limited literature. In addition, another new imaging mode was introduced and analyzed based on NGP. The corresponding projection model [nonstandard normalized geostationary projection (NNGP)] was proposed, which is entirely consistent with the situation of America’s Geostationary Operational Environmental Satellite-R Series (GOES-R) and Chinese Fengyun-4A (FY-4A). Furthermore, this paper proposed a novel nominal projection model for frame imaging, which is consistent with the imaging mode of China’s Gaofen-4. Finally, extensive experiments were designed to comparatively analyze the three nominal grids and demonstrate a detailed difference. By providing a theoretical basis for nominal grid selection, this research is highly significant for the efficient near-real-time production and further applications of geostationary images, as well as the conversion between different datasets resulting from different nominal grid data. In addition, our models are sufficiently tested during the on-orbit running of FY-4A, the first satellite of China’s second-generation three-axis stabilized geostationary meteorological satellite series. The algorithms provide the technical support for the high-precision image navigation and registration and play a significant role in robustly producing the meteorological data with similar quality to those from GOES-R.