National Administration of Surveying, Mapping and Geoinformation of China performed the first order leveling campaign for the second time from 1991 to 1999 and the latest leveling campaign from 2012 to 2015, respectively. The time span of two leveling campaigns was about 20 years, due to the influence of crustal movement, economic construction, groundwater exploitation and recharge, the ground surface settlement with different degree and seasonal surface relieves occurred in some regions, which led to various changes of regional elevation and affected the maintenance of height datum and the application of elevation results. The first order leveling coincidence points in the two periods and their changes and trends were analyzed. The reasons for the change of elevation were analyzed from four aspects, which are the difference of measuring equipment, measures and measuring accuracy of leveling network in two periods, the difference of gravity datum, normal gravity formula and gravity data adopted by correcting computation of gravity anomaly, the difference of configuration structure, the difference of leveling period and closure period of leveling ring as well as the influence of vertical crustal movement and local surface deformation. Through this analysis, we can conclude that the serious ground surface settlement occurred in some regions during the recent 20 years. The vertical crustal movement and local surface deformation are the major factors affecting the elevation changes. The first order leveling should be monitored on a regular period which should be no more than 5 years and strives for 3 years.
Because there are some shortcomings in the current MHSS ARAIM algorithm, such as the weaker robustness, too many computational subsets and large amount of computation, a multiple hypothesis solution separation advanced receiver autonomous integrity monitoring (MHSS ARAIM) algorithm combined with gross error detection is proposed in this paper. With this new algorithm, the gross error detection method is used to identify and eliminate the gross data in the original data first. Then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection. Therefore, this new algorithm can make up for the weakness of the MHSS ARAIM algorithm. Through the data processing and analysis from several IGS and international GNSS monitoring and assessment system (iGMAS) stations, the results show that this new algorithm is superior to MHSS ARAIM in the aviation phase of LPV-200 when it is used in the navigation with GPS and BDS. And under the assumption of a faulty satellite, accuracy of the effective monitoring threshold (EMT) is improved about 22.47% and 9.63%, and accuracy of the vertical protection level (VPL) is improved about 32.28% and 12.98%respectively for GPS and BDS observations respectively. Moreover,under the assumption of two faulty satellites, accuracy of the EMT is improved about 80.85% and 29.88%, and accuracy of the VPL is improved about 49.66% and 18.24% for GPS and BDS observations respectively.
Traditional polar navigation usually uses grid navigation to implement great circle,which could cause problems that different grid direction of great circle makes against navigation control and great circle is not straight lines on the polar projection lead to principle error.To solve these problems, considering the idea that it is easier to navigaition control and plotting if the rumble route in the lower latitudes is straight lines on the mercator projection, a rumble route called grid rumble route which are straight lines on polar stereographic projection is suggested.Firstly, polar stereographic projection based on double projection and grid navigation are studied.Then the definition of grid rumble route and route equation is presented.Finally, compitational method of distance and grid direction for grid rumble route is provided.From the theoretical analysis and simulations, grid rumble route is approximate to great circle and great ellipse routes,the navigation distance is relatively short; thus, they are straight lines on the polar stereographic projection and grid direction is equal everywhere. Moreover, they can be used in conjunction with grid navigation method and polar stereographic projection, more applicable for the polar navigation.
Interferogram denoising plays an important role to the application of InSAR technique. If the phase noise cannot be well filtered, the phase unwrapping error is frequently arisen, which will further result in the mistakes in the DEM product and the deformation result. The complex value of each SAR resolution unit is superimposed by the phases from different scatterers, so the paper focuses on the characteristics of single dominant phase scattering model (the permanent scatterer) and traditional distributed scatterer of single scattering mechanism. Then the robust covariance matrix, estimated based on multi-baseline SAR data, is decomposed and the eigenvector corresponding to the maximum eigenvalue is chosen as the filtered phase. Besides, the covariance matrix is robustly estimated by weighted averaging the heterogeneous points. This method can operate better than the improved Goldstein filter algorithm in the terms of coherence improvement and effective coherent targets increasing, especially in the low-coherence regions. Eight real TerraSAR-X data over one land subsidence region, Qingxu, Shanxi verifies the advantages of our new method.
Traditional building extraction from very high resolution remote sensing optical imagery is limited by low precision and incomplete boundary. In this paper, a high-resolution remote sensing image building extraction method based on deep learning is proposed. Firstly, Principal Component Analysis is used to pre-train network structure in an unsupervised way and obtain the characteristics of remote sensing image. Secondly, an adaptive pooling model is proposed to reduce the feature information loss in the pooling process. The texture features are extracted by non-subsampled contour wave transformation and introduced to the network to improve the building extraction. Finally, the obtained image features are inputted into the softmax classifier for classification and building extraction results. A typical experiment areas selected. The comparison with typical building extraction method, the experimental results shows that the proposed method can extract the buildings with higher accuracy, especially the clearer and more complete boundary.
Volume parameter is the basic content of a spatial body object morphology analysis. However, the challenge lies in the volume calculation of irregular objects. The point cloud slicing method proposed in this study effectively works in calculating the volume of the point cloud of the spatial object obtained through three-dimensional laser scanner (3DLS). In this method, a uniformly spaced sequent slicing process is first conducted in a specific direction on the point cloud of the spatial object obtained through 3DLS. A series of discrete point cloud slices corresponding to the point cloud bodies are then obtained. Subsequently, the outline boundary polygon of the point cloud slicing is searched one by one in accordance with the slicing sequence and areas of the polygon. The point cloud slice is also calculated. Finally, the individual point cloud section volume is calculated through the slicing areas and the adjacent slicing gap. Thus, the total volume of the scanned spatial object can be calculated by summing up the individual volumes. According to the results and analysis of the calculated examples, the slice-based volume-calculating method for the point cloud of irregular objects obtained through 3DLS is correct, concise in process, reliable in results, efficient in calculation methods, and controllable in accuracy. This method comes as a good solution to the volume calculation of irregular objects.
A classification method of hyperspectral images based on deep 3D convolution networks is proposed in order to deal with the high dimensional and small samples of hyperspectral image classification. The method first uses hyperspectral data cube as input, and uses 3D convolution operation to extract 3D spatial-spectral features of hyperspectral data cube. Then, the residual learning is used to construct the deep network and extract higher level feature expression to improve the classification accuracy. Finally, the Dropout regularization method is used to prevent overfitting. Experiments were conducted on the University of Pavia, Indian Pines and Salinas datasets, and the results demonstrate that compared with support vector machine and the existing deep learning classification method for hyperspectral images, the method can effectively improve the classification accuracy of hyperspectral image.
It is proposed that a high-resolution remote sensing image segmentation method that combines static minimum spanning tree tessellation considering shape information and the RHMRF-FCM algorithm. It solves the problems in traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in complex boundary exist. By using the MST model and shape information, the object boundary and geometrical noise can be expressed and reduced respectively. Firstly, the static MST tessellation is employed for partitioning the image domain into some polygons corresponded to the components of homogeneous regions needed to be segmented. Secondly, based on the tessellation results, the RHMRF model is built, and regulation term considering the KL information and information entropy are introduced into the FCM objective function. Finally, the partial differential method is employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results. To verify the robust and effective of proposed algorithm, the experiments are carried out with WorldView-3 high resolution image. The results from proposed method with different parameters and comparing methods (the multi-resolution and the watershed segmentation method in eCognition software) are analyzed qualitatively and quantitatively.
For the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion, this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level. Firstly, this approach analyzed taxis' operating patterns and filtered valid traces. Then this approach detected traffic congestion traces of three different intensities:mild congestion, moderate congestion and serious congestion, based on analyzing traffic conditions from the filtered valid trace segments. Finally, traffic flow speed, congestion time and congestion distance of each turning direction at an intersection were explored at a fine-grained level. The experimental results show that the proposed approach is able to detect congestions of different intensities and analyze congestion events at turning level.
Node importance has significant influence on the calculation of shortest path of large-scale road network. A shortest path estimation method based on node importance is proposed in this paper that is suitable for large-scale network. This method integrates the criteria importance though intercrieria correlation (CRITIC) method with complex network theory, with a view to evaluate nodes importance. By combining the restriction strategy to realize network division, the effective simplification of large-scale road network and shortest path estimation are realized through the construction of hierarchical network. The results show that this method can be used to distribute the center nodes evenly, and make little difference in the size of the subnetwork. As the constraint parameter increases, the numbers of nodes and edges reduced gradually, and the query accuracy reached 1.026. Compared with single index and unlimited parameters methods, this paper significantly reduces the size of the network and obtains a high accuracy on the approximate calculation of the shortest path. These will provide a new way of thinking for approximate analysis of large-scale complex networks.
Discovery of co-location patterns is crucial to understanding the interaction among different spatial features. The construction of neighborhood relationship among spatial features plays a key role in co-location pattern mining, however, existing methods are difficult to construct appropriate neighborhood relationship when the spatial features distribute unevenly.This limitation is very likely to make the omission and/or misjudgment of co-location patterns.To address this issue, a co-location pattern mining method based on natural neighborhood is proposed in this study.After removing the randomly distributed spatial features,natural neighborhood relationship among different spatial features is adaptively constructed on basis of three principles, i.e. geographic proximity, the consistency of density and compactness of neighboring relationship. The multi-level co-location patterns are discovered based on the delaunay triangulation network. The experimental results showed that the proposed method could discover the co-location patterns among unevenly distributed spatial features completely and accurately, and no user-specified parameters are required for the construction of natural neighborhood.
The ground deformation monitoring based on permanent scatterers interferometric synthetic aperture radar (PSInSAR) is limited by the spatial distribution density of the PS, especially in non-urban areas with sparse PS distribution. To address this, an improved algorithm is proposed based on temporarily coherent targets(TCT) which maintain high coherence of partial observation period and whose spatial distribution is rich in non-urban areas. After incorporating the seasonal characteristics of ground scatterers, the algorithm screens the interferometric pair to preserve the TCT which can be distinguished by dual-threshold method. After mining and separating the interferometric phase information by multi-differential process, the deformation velocity and elevation correction of TCT are inversed. Twenty-four Sentinel-1A SAR images, acquired between 2014-10-24 and 2016-05-09, are processed to extract the ground deformation of TCT in Jingbian County, Shaanxi Province. Compared with PSInSAR, the results provide evidence that multitemporal analysis method of temporarily coherent targets can significantly increase the spatial distribution density of monitoring targets, as well as effectively and reliably monitor the ground deformation in non-urban areas.
This paper focuses on solving the problem of seafloor control point absolute positioning with low vertical accuracy based on the survey ship sailing circle. The method of dealing with the systematic error based on semi-parametric adjustment model was proposed. Firstly, the influence of sound speed change on ranging error is analyzed. Secondly, a semi-parametric adjustment model for determining three-dimensional coordinates of underwater control points was established. And respectively proposed solutions under two different conditions, the observation duration is an integral multiple or non-integer multiple of the long-period term of the ranging error. Simulation experiment results show that this method can obviously improve the accuracy of vertical solution of seafloor control point compared with difference technique and least square method when internal waves exist and observation duration is less than an integer multiple of the long-period term of the ranging error.
Backscatter strength angular response embodies the seafloor substrates classification, which are the important data sources for multibeam seafloor classification. At present, multibeam seafloor classification mainly extract mean backscatter strength angular response or sonar image without considering the relevant information of two aspects. This paper comprehensively analyzes backscatter strength angular response and probability distribution of backscatter strength, portraits 3D histogram, and proposes ways of seafloor classification based on 3D histogram of backscatter strength. Results show that the method can directly express different numbers of seafloor classification within multibeam swath, effectively judge the boundaries and realize the fast identification of different seafloor substrates.
