The rate of the total electron content (TEC)change index (ROTI)can be regarded as an effective indicator of the level of ionospheric scintillation, in particular in low and high latitude regions. An accurate prediction of the ROTI is essential to reduce the impact of the ionospheric scintillation on earth observation systems, such as the global navigation satellite systems. However, it is difficult to predict the ROTI with high accuracy because of the complexity of the ionosphere. In this study, advanced machine learning methods have been investigated for ROTI prediction over a station at high-latitude in Canada. These methods are used to predict the ROTI in the next 5 minutes using the data derived from the past 15 minutes at the same location. Experimental results show that the method of the bidirectional gated recurrent unit network (BGRU)outperforms the other six approaches tested in the research. It is also confirmed that the RMSEs of the predicted ROTI using the BGRU method in all four seasons of 2017 are less than 0.05 TECU/min. It is demonstrated that the BGRU method exhibits a high level of robustness in dealing with abrupt solar activities.

According to the characteristics of the road features, an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images. Firstly, as the features of the road target are rich in local details and simple in semantic features, an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information. Secondly, as the road area is a small proportion in remote sensing images, the cross-entropy loss function is improved, which solves the imbalance between positive and negative samples in the training process. Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%, precision 82.5% and F1-score 82.9%, which can extract the road targets in remote sensing images completely and accurately. The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation, so it has a good application prospect.

The laser scanning system based on Simultaneous Localization and Mapping (SLAM)technology has the advantages of low cost, high precision and high efficiency. It has drawn wide attention in the field of surveying and mapping in recent years. Although real-time data acquisition can be achieved using SLAM technology, the precision of the data can’t be ensured, and inconsistency exists in the acquired point cloud. In order to improve the precision of the point cloud obtained by this kind of system, this paper presents a hierarchical point cloud global optimization algorithm. Firstly, the “point-to-plane” iterative closest point (ICP) algorithm is used to match the overlapping point clouds to form constraints between the trajectories of the scanning system. Then a pose graph is constructed to optimize the trajectory. Finally, the optimized trajectory is used to refine the point cloud. The computational efficiency is improved by decomposing the optimization process into two levels, i.e. local level and global level. The experimental results show that the RMSE of the distance between the corresponding points in overlapping areas is reduced by about 50% after optimization, and the internal inconsistency is effectively eliminated.

The influence of the single photon laser altimeter range-gate width on the detection probability and ranging accuracy is discussed and analyzed, according to the LiDAR equation, single photon detection equation and the Monte Carlo method to simulate the experiment. The simulated results show that the probability of detection is not affected by the range gate, while the probability of false alarm is relative to the gate width. When the gate width is 100ns, the ranging accuracy can accord with the requirements of satellite laser altimeter. But when the range gate width exceeds 400ns, ranging accuracy will decline sharply. The noise ratio will be more as long as the range gate to get larger, so the refined filtering algorithm during the data processing is important to extract the useful photons effectively. In order to ensure repeated observation of the same point for 25 times, we deduce the quantitative relation between the footprint size, footprint, and frequency repetition according to the parameters of ICESat-2. The related conclusions can provide some references for the design and the development of the domestic single photon laser altimetry satellite.

Solar radiation pressure (SRP) model is the basis of high precise orbit determination and positioning of navigation satellites. At present, it is common to see the study of SRP model of BDS satellites. However, the establishment and application of a comprehensive analytical SRP model based on satellite physical parameters are rare. Different from other conservative forces and non-conservative forces, SRP is closely related to the satellite’s physical parameters and in-orbit state. On the basis of the physical mechanism of solar radiation, BDS satellite physical parameters, in-orbit attitude control mode, and so on, a comprehensive analytical model has been studied in this paper. Based on precise ephemeris and satellite laser ranging (SLR) data, the precision of a comprehensive analytical model has been verified. And the precision of orbit determination is at the decimeter level using this comprehensive analytical SRP model. According to the satellite conservation theorem of angular momentum and change of in-orbit telemetry parameters, the difference between a comprehensive analytical model and the actual in-orbit interference force has been analyzed and calculated. The addition of empirical items on the comprehensive analytical model has been proposed. SLR validations demonstrated that the orbit precision of BDS C08 and C10 can be achieved at 0.078m and 0.084m respectively. Compared with using the improved CODE empirical model, precision orbit accuracy of them has increased by 0.021m and 0.045m respectively.

For classical TCAR(three carrier ambiguity resolution) algorithm is affected by ionospheric delay and measurement noise, it is difficult to reliably fix ambiguity at medium and long baselines. An improved TCAR algorithm which takes the influence of ionospheric delay into account and has good adaptive robustness is proposed. On the basis of the non-geometric TCAR model, ionospheric delay is obtained by linearly combining extra-wide-lane with fixed ambiguity, and then wide-lane ambiguity is solved. Solving narrow-lane ambiguity by adaptive robust filtering by constructing optimal combination observation, which can effectively improve the fixed success rate of narrow-lane ambiguity and reduce the adverse effects of gross error. Experimental results show that the improved TCAR algorithm can guarantee a high fixed correct rate of wide-lane ambiguity, effectively improve fixed success rate of narrow-lane ambiguity, and has a good ability to resist gross error.

The fast convergence without initial value dependence is the key to solving large angle relative orientation. Therefore, a hybrid conjugate gradient algorithm is proposed in this paper. The concrete process is: ① stochastic hill climbing(SHC) algorithm is used to make a random disturbance to the given initial value of the relative orientation element, and the new value to guarantee the optimization direction is generated. ②In local optimization, a super-linear convergent conjugate gradient method is used to replace the steepest descent method in relative orientation to improve its convergence rate. ③The global convergence condition is that the calculation error is less than the prescribed limit error. The comparison experiment shows that the method proposed in this paper is independent of the initial value, and has higher accuracy and fewer iterations.

To explore the problem of distance transformations while obstacles existing, this paper presents an obstacle-avoiding Euclidean distance transform method based on cellular automata. This research took the South China Sea and its adjacent sea areas as an example, imported the data of land-sea distribution and target points, took the length of the shortest obstacle-avoiding path from current cell to the target cells as the state of a cellular, designed the state transform rule of each cellular that considering a distance operator, then simulated the propagation of obstacle-avoiding distance, and got the result raster of obstacle-avoiding distance transform. After analyzing the effect and precision of obstacle avoiding, we reached the following conclusions: first, the presented method can visually and dynamically show the process of obstacle-avoiding distance transform, and automatically calculate the shortest distance bypass the land; second, the method has auto-update mechanism and each cellular can rectify distance value according to its neighbor cellular during the simulation process; at last, it provides an approximate solution for exact obstacle-avoiding Euclidean distance transform and the proportional error is less than 1.96%. The proposed method can apply to the fields of shipping routes design, maritime search and rescue, etc.

Shallow water multi-beam echo sounders (MBESs) are characterized by their high resolution and high density, and MBES data processing is a hotspot in modern marine surveying. The Combined Uncertainty and Bathymetry Estimator (CUBE) is the mainstream MBES data processing algorithm, although little is known about its core theories and parameters. In this paper, the basic principle, mathematical model, key parameters, and main processing steps of CUBE are described systematically. A parameter group optimization method that combines CUBE with a surface filter is established. Additionally, an example is given that shows the steps for parameter group optimization, including selection of a typical area, parameter group testing, and comparative analysis, and the method is then applied to shallow water MBES data processing. The results show that the method can improve the accuracy and efficiency of automatic data processing effectively, and it is thus of engineering application value.

For the current automatic coastline generalization method, only one-line element is considered separately, but the relationship between the nearby elements is not effectively considered. A synergistic simplification method for multiple coastlines based on the hierarchical triangulation network partition (HTNP) is proposed in this paper. Firstly, the constrained Delaunay triangulation is constructed to partition the regions that can be simplified. Then, a hierarchical binary tree model to structure the morphological characteristics of the above several coastlines and the spatial proximity between different coastlines is constructed. Finally, the small curved and curved invisible parts of the coastline are deleted according to the visual constraints, and the narrow part between the coastline itself or the different coastlines is exaggerated appropriately, and the automatic simplification of the coastline is realized. The experimental results show that: ① Relationships between the different coastlines are considered, and the shortcomings of considering the coastline separately are overcome; ② Under the condition of the multiple coastlines in complex sea areas, the problem of collaborative simplification is solved, the quality of the coastlines are improved obviously, and the proposed method can be applied into more types of coastlines.

Pan-sharpening is a process of obtaining a high spatial and spectral multispectral image (HMS) by combining a low-resolution multispectral image (LMS) with a high-resolution panchromatic image (PAN). In this paper, a pan-sharpening method called PAIHS is proposed, which is based on adaptive intensity-hue-saturation (AIHS) transformation, variational pan-sharpening framework and the two fidelity hypotheses. The suitable objective function is established and optimized by adopting particle swarm optimization (PSO) to obtain the optimal control parameters and minimum value. This value corresponds to the best pan-sharpening quality. The experimental results show that the proposed method has high efficiency and reliability, and the obtained performance index is superior to the four mainstream pan-sharpening methods.