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Unmanned aerial vehicles (UAVs) are becoming increasingly popular in various fields such as agriculture, forest protection, resource exploration, and so on, due to their ability to capture high-resolution images quickly and efficiently at low altitudes. However, real-time image mosaicking of UAV image sequences, especially during long multi-strip flights, remains challenging. In this paper, a real-time incremental UAV image mosaicking framework is proposed, which only uses the UAV image sequence, and does not rely on global positioning system (GPS), ground control points (CGPs), or other auxiliary information. Our framework aims to reduce spatial distortion, increase the speed of the operation in the mosaicking process, and output high-quality panorama. To achieve this goal, we employ several strategies. First, the framework estimates the approximate position of each newly added frame and selects keyframes to improve efficiency. Then, the matching relationship between keyframes and other frames is obtained by using the estimated position. After that, a new optimization method based on minimizing weighted reprojection errors is adopted to carry out precise position calculation of the current frame, so as to reduce the deformation caused by cumulative errors. Finally, the weighted partition fusion method based on the Laplacian pyramid is used to fuse and update the local image in real time to achieve the best mosaic result. We have carried out a series of experiments which show that our system can output high-quality panorama in real time. The proposed keyframe selection strategy and local optimization strategy can minimize cumulative errors, the image fusion strategy is highly robust, and it can effectively improve the panorama quality. [ABSTRACT FROM AUTHOR]

Asia-Pacific ground-based Optical Space object Observation System (APOSOS) is an international cooperation project, which is initiated and supported by Asia-Pacific Space Cooperation Organization (APSCO). Currently, APOSOS consists of three Observation Nodes in Pakistan, Peru and Iran, and a Data and Operation Management Center in National Astronomical Observatories, Chinese Academy of Sciences. Each Observation Node is equipped with a dedicated 15 cm aperture refracting telescope, which is capable of real-time astrometric data reduction. APOSOS aims to develop a tracking network of space object to acquire accurate astrometric measurements of space objects owned by APSCO member states. In this paper, we present an overview of APOSOS's design and development. The APOSOS's detecting capability of tracking a space object is darker than 12 m in LEO or 16 m in GEO, the size of which is equivalent to a diameter of 10-cm class in LEO or meter class in GEO respectively. A parametric estimation model is setup to profile APOSOS's capability in different tracking missions, which is verified by experimental observations. Preliminary results of tracking observations of SLR satellites showed that RMS of APOSOS astrometric measurements in RA/DEC is better than 5 ″ . [ABSTRACT FROM AUTHOR]

The bistatic radar system has been one of the effective methods to detect the space debris in low earth orbit (LEO). Tianlai radio array with cylindrical-parabolic antennas is designed for dark energy detection, which has large field of view and high sensitivity, offering a fan-beam during the observation. We propose a bistatic radar system, which consists of Tianlai radio array and an incoherent scattering radar (ISR) assumed as a transmitter in the Qujing city of China, to detect space debris. In this paper, we calculate and analyze the detection capabilities of this system. The results show the bistatic radar system has the potential to detect small space debris of less than 10 cm in LEO. We provide a space debris detection method to obtain the position of the cross-beam satisfying the observation requirement with the TLE data of the space debris. The method can solve the problem of space synchronization between the radio array and ISR. We used the long-short baseline method of the radio array to locate the space target. The relationship among positioning error, the azimuth and the elevation angle are also discussed. [ABSTRACT FROM AUTHOR]

The signal-to-noise ratio (SNR) of debris laser ranging (DLR) data is extremely low, and the valid returns in the DLR range residuals are distributed on a curve in a long observation time. Therefore, it is hard to extract the signals from noise in the Observed-minus-Calculated (O-C) residuals with low SNR. In order to autonomously extract the valid returns, we propose a new algorithm based on randomized Hough transform (RHT). We firstly pre-process the data using histogram method to find the zonal area that contains all the possible signals to reduce large amount of noise. Then the data is processed with RHT algorithm to find the curve that the signal points are distributed on. A new parameter update strategy is introduced in the RHT to get the best parameters. We also analyze the values of the parameters in the algorithm. We test our algorithm on the 10 Hz repetition rate DLR data from Yunnan Observatory and 100 Hz repetition rate DLR data from Graz SLR station. For 10 Hz DLR data with relative larger and similar range gate, we can process it in real time and extract all the signals autonomously with a few false readings. For 100 Hz DLR data with longer observation time, we autonomously post-process DLR data of 0.9%, 2.7%, 8% and 33% return rate with high reliability. The extracted points contain almost all signals and a low percentage of noise. Additional noise is added to 10 Hz DLR data to get lower return rate data. The valid returns can also be well extracted for DLR data with 0.18% and 0.1% return rate. [ABSTRACT FROM AUTHOR]

With the proposition of the Digital Earth (DE) concept, Virtual Geographic Information System (VGIS) has started to play the role of a Digital Earth prototype system. Many core problems involved in VGIS, such as out-of-core management and interactive rendering of very large scale terrain and image data, have been well studied in the past decades. However, the jitter problem, a common problem in VGIS that often causes annoying visual artefacts and deteriorates the output image quality, draws little attention. In this paper, after an intensive analysis of the jitter problem, a comprehensive framework is proposed to address such a problem while accounting for the characteristics of different data types in VGIS, such as terrain or ocean mesh data, vector data and 3-D model data. Specifically, this framework provides an improved dynamic local coordinate system (DLCS) method for terrain or ocean mesh data. For vector data, the framework provides a simple and effective multiple local coordinate systems (MLCS) method. The framework provides a MLCS method for 3-D model data making full use of the existing local coordinate system of the model. The advantages of the proposed methods over current approaches are analysed and highlighted through case studies involving large GIS datasets. [ABSTRACT FROM AUTHOR]