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This paper introduces the extended collinearity equations into aerial triangulation of the vehicle-borne multi-camera rig images to improve the positioning accuracy. The cameras in a multi-camera rig are rigidly fixed and the relative position and orientation parameters among the mono cameras of multi-camera rig can be calibrated accurately, so the extended collinearity equations can be used to extend the imaging unit from a mono camera image to multiple images of multi-camera rigs. Compared with the existing spherical models, including the spherical ideal model and the spherical rigorous model, the extended collinearity equations used in this paper avoid the spherical projection error and fusion error caused by the misalignment of projection centers. Experimental results show that the method omits the processing procedure of model projection and fusion of overlap areas, which avoids the precision loss and complicated processing procedure, and finally obtains more robustness triangulation net, more precise and robust position accuracy.

Road network matching is an important prerequisite for the change detection and data updating of spatial database, and the matching of road networks at different scales is very important. In this paper, the existing algorithms road networks matching are summarized and analyzed firstly, and according to the problems and difficulties in the road networks matching at different scales, an algorithm integrating multiple matching techniques was designed. Based on the characteristics of road networks at different scales, the method of evaluating the structure of spatial scene was improved. The limitations of the algorithm based on stroke matching were analyzed for road networks data at the different scales, and the algorithm named “partial stroke matching” was put forward. The experiments indicate that the algorithm given in this paper can be used in matching of road networks at different scales, the effect of matching is good, and the running efficiency is high as well.

Image dodging method is one of the important processes that determines whether the mosaicking image can be used for remote sensing quantitative application. GF-1 satellite is the first satellite in CHEOS (Chinese high-resolution earth observation system). WFV multispectral sensor is one of the instruments onboard GF-1 satellite which consist of four cameras to mosaic imaging. According to the characteristics of WFV sensor, this paper proposes an image dodging algorithm based on cross/inter-radiometric calibration method. First, the traditional cross calibration method is applied to obtain the calibration coefficients of one WFV camera. Then statistical analysis and simulation methods are adopted to build the correlation models of DN and TOA (top of atmosphere) radiances between adjacent cameras. The proposed method can not only accomplish the radiation performance transfer, but also can fulfill the image dodging. The experimental results show the cross/inter-radiometric calibration coefficients in this paper can effectively eliminate the radiation inconsistency problem of the adjacent camera image which realizes the image dodging. So our proposed dodging method can provide an important reference for other similar sensor in future.

The existing visualization method in the virtual globe mainly uses the projection grid to organize the ocean grid. This special grid organization has the defects in reflecting the difference characteristics of different ocean areas. The method of global ocean visualization based on global discrete grid can make up the defect of the projection grid method by matching with the discrete space of the virtual globe, so it is more suitable for the virtual ocean surface simulation application.But the available global discrete grids method has many problems which limiting its application such as the low efficiency of rendering and loading, the need of repairing grid crevices. To this point, we propose an optimization for the global discrete grids method. At first, a GPU-oriented multi-scale grid model of ocean surface which develops on the foundation of global discrete grids was designed to organize and manage the ocean surface grids. Then, in order to achieve the wind-drive wave dynamic rendering, this paper proposes a dynamic wave rendering method based on the multi-scale ocean surface grid model to support real-time wind field updating. At the same time, considering the effect of repairing grid crevices on the system efficiency, this paper presents an efficient method for repairing ocean surface grid crevices based on the characteristics of ocean grid and GPU technology. At last, the feasibility and validity of the method are verified by the comparison experiment. The experimental results show that the proposed method is efficient, stable and fast, and can compensate for the lack of function of the existing methods, so the application range is more extensive.

Cellular Automata (CA) has been widely adopted in atmosphere, hydrodynamics, geophysics and geoscience for its simple structure and capability of simulating complex system. Existing CAs in geoscience ignore the restraints of gravity, and do earth system processes simulation under the constraints of Euclidean, which is not in accord with the tendency of state delivery from one cell to cell, and will produce some distortions on the results. This paper is to propose an Earth System Cellular Automata (ESCA) based on the restraint of gravity. The framework, including cell representation and construction, and neighbor model based on SDOG-ESSG, was proposed in the paper. As evolution rules are highly depended on actual problems, the problem of heat conduction in the Crust was taken as an example to design an evolution rule for the ESCA by imposing a discretization on thermodynamics law. Finally, a preliminary experiment and a comparison with numerical simulation in local scale were carried out. Results showed that, ESCA is reliable to some extent with its relative error limited to 27% comparing with numerical simulation. ESCA can be a new way of simulation for earth system process.

The Discrete Global Grid Systems (or the global position location framework) is a kind of scientific and concise spatial reference system, which is the discretization, accurate and systematic representation of the earth information data. In recent years, with the development of GPS and Beidou satellite system and the process of globalization, the global metric and accuracy of spatial analysis should be pay more attention by people. In this paper, first, for the progress and insufficiency over recent years of the earth grid research, the author discusses several quantity issues include:the integrity of meshing, logical consistency, and some related problems. Then regarding the questions of position and metric, the author points out that the Earth is the target object of meshing its characteristics of metric space. The revolutionary demand of "3D structure and centimeter level accuracy" will have a significant impact on global GIS system. Next, the 3D meshing approaches to the ellipsoid of the earth are stressed, and the author also introduces the corresponding experiments and theoretical approach in map algebra on the 3D earth meshing. Finally, this paper reaffirms that the 3 sets of (B, L, H) orthogonal surface should be the most appropriate way on metric for the Earth meshing.

Currently, the cloud storage platform based on key-value model can only support simple keyword queries but cannot support multidimensional spatial queries. To solve the problem, this paper puts forward a new method of distributed spatial index-M-Quadtree index. In the process of index building, a space partitioning method based on improved quadtree was proposed. This partitioning method specifies the minimum amount of data in the leaf area. By recombining the quad leaves, it solves the problem of storage imbalance among sub regions, and meets the parallel requirements of the MapReduce. This paper describes some algorithms about M-Quadtree index building,querying and updating under the MapReduce framework. In the experiments, we implement the M-Quadtree index on Hadoop platform to test the effect of key parameter on the efficiency of index, and also test the efficiency of index building, querying and updating under different scale of data. Comparing with existing distributed spatial index, experiments show that the M-Quadtree index performs better on data load balancing, algorithm parallelism and the efficiency of spatial querying.

It has been paid attention to improving the elevation accuracy of satellite stereo images aided by laser altimeter. The GF-7 satellite scheduled to launch in 2018 will be equipped with optical stereo cameras and a laser altimeter. ICESat (Ice, Cloud, and land Elevation Satellite) with GLAS(Geo-science Laser Altimeter System) is the first and still only laser altimeter satellite for earth observation until now. In this paper, the comprehensively analysis about the rigorous geometric model and accuracy analysis of laser altimeter is presented. The error induced by laser pointing aberration and mounting is proposed, and the data processing workflow of ICESat/GLAS from level 0 to level 2 is introduced. What's more, the geo-location accuracy between this paper and GLAS product is compared and the model is validated by the result that the accuracy based on the model is about 3 cm and 11 cm in the horizontal and vertical direction, respectively. The laser altimeter data loaded on the ZY3-02 satellite has been processed and validated preliminarily. The conclusion of this paper is valuable and can be viewed as reference for the subsequent domestic laser altimeter satellites.

Real-time high-precise satellite orbit and clock products are needed in real-time GNSS precise point positioning (PPP). In this paper, Estimation model and strategy of multi-GNSS precise satellite clock integrated resolving are researched and BeiDou/GPS real-time precise clock integrated estimation algorithm is realized by filter. Real-time simulation test results show: the STD accuracy of BeiDou/GPS real-time clock estimated in this paper compared to GFZ multi-GNSS precise clock(GBM) is about 0.15ns; horizontal accuracy after convergence of GPS kinematic PPP using simulation real-time clock products estimated in this paper is better than 5cm and vertical accuracy is better than 10cm, respectively; in BeiDou kinematic PPP test, horizontal and vertical accuracy results are same as the results using GFZ multi-GNSS precise clock(GBM) products, and the decimeter positioning can be realized.

Trajectory stops imply important semantic information, and the extraction of trajectory stops is the premise to carry out advanced Stop/Move analysis. This paper, based on the idea of kernel density, firstly introduces the concept of stop index, which is derived by cumulating spatio-temporal contribution of neighboring points, and further designs stop index graph to intuitively visualize the evolution of spatio-clustering degree during a trajectory. Importantly, stops index and its graph are related to spatial scale through neighboring radius, which then can be exploited to analyze trajectory stops under multiple scales. In addition, this paper introduces stop sequence rooted from stop index, and proposes an algorithm for the automatic extraction of trajectory stops by progressively merging stop sequences. According to the algorithm, a stop under strong GPS signal exactly corresponds to a stop sequence, while a stop under weak GPS signal could be derived by merging multiple stop sequences. Experiments based on own-acquired and GeoLife trajectories show that the algorithm has achieves the balance between the completeness and accuracy of stop extraction, and could effectively discover and extract complex and diverse trajectory stops. Even facing trajectories with serious drift noises, the algorithm still achieves a high rate of accuracy on stop extraction.

Lunar/Mars rovers own sun sensors for navigation, however, long-time tracking for the sun impacts on the real-time activity of navigation. Absolute positioning method by observing the sun with a super short tracking period such as 1 or 2 minutes is researched in this paper. Linear least squares model of altitude positioning method is deduced, and the ill-posed problem of celestial positioning using the sun is brought out for the first time. Singular value decomposition method is used to diagnose the ill-posed problem, and different bias estimations are employed and compared by simulative calculations. Results of the calculations indicate the superiority of bias estimations which can effectively improve initial values. However, bias estimations are greatly impacted by initial values, because the initial values converge at a line which passes by the real value and is vertical relative to the orientation of the sun. The research of this paper is of some value to application.

This paper proposes an optimization method for computing the radial gravity gradient by using GOCE gradient measurements, based on the variance-covariance information of gradient observations. The approach for computing the gravity gradient and the error propagation are firstly discussed; and then by solving a conditional extremum problem, we can get an optimal combination factor which can improve the calculation accuracy for the radial gravity gradient. The advantage of the combination factor is validated by simulation data. In actual data processing, the geoid accuracy truncated to degree 250 can be improved by 2 cm by using the optimization method. The radial gravity gradient can not only be used in recovering a gravity field model, but also can be used in kinds of geophysical interpretation, so the method provided in the paper can be helpful in the related researches.

GNSS-R is a new technology for monitoring the water level with high efficiency. Compared with conventional water level measurement technique, such as satellite altimetry and tide gauges, GNSS-R can observe more reflected points with high temporal and spatial resolution and unaffected by the influence of the plate vertical motion. This paper presented an improved cGNSS-R altimetry algorithm based on single difference to derive the reflector heights epoch-by-epoch, which can enhance the temporal and spatial resolution of surface height measurements, furthermore, the other algorithm based on the double differenced carrier phase measurements was also presented in this paper. By using the observed data of cGNSS-R altimetry experiment conducted on Qinghe bridge of East Lake, Wuhan, the reflector heights between the reflected antenna and the lake surface were given to prove the above mentioned algorithms, and the precision were ±2~±4 cm. The results show that the proposed algorithms based on single and double difference which are used for water level retrieval can sufficiently decrease the influences due to clock error, ionospheric and tropospheric error.

Information extraction from remote sensing image is the key to remote sensing applications and scale selection is one of the key factors influencing the information extraction accuracy. This paper discusses the theoretical foundation of applying spatial statistical methods to resolve the scale related issues involved in remote sensing pattern classification. Aiming at geo-object-based image analysis (GeOBIA), scale parameters involved in multi-scale segmentation for GeOBIA are generalized into three ones, and they are spatial parameter, attribute parameter and merging threshold. Further, the pre-estimation method of the optimal scale parameters is proposed based on spatial statistics. Taking GeOBIA based cropland extraction from SPOT-5 image as an example, this paper proposes a cropland extraction method combining spatial statistics based adaptive scale parameter pre-estimation and object-oriented classification. This paper employs mean-shift segmentation and series Rof object based classification on different scales to verify the validity of this method. Experimental results support the object based cropland extraction method based on the data-driven scale pre-estimation. The cropland extraction result by using the pre-estimated segmentation parameters can guarantee the accuracy of GeOBIA classification and the cropland extraction based on GeOBIA and adaptive scale pre-estimation avoids the time-consuming trial-and-error practice and speeds up the object-oriented classification procedure.

Spherical panoramic imaging can overcome the limitations of perspective imaging's range of view and achieve three-dimensional reconstruction and measurement with full coverage.The EPnP(efficient perspective-n-point) algorithm fit for ordinary image's position and pose estimation is improved and extended by this paper to put forward a robust and fast method to estimate spherical panoramic image's position and pose. Firstly,we construct the projection model of spherical panoramic image to extend the perspective plane imaging model of EPnP algorithm to the spherical imaging model;then with the improved EPnP algorithm basing on the condition equation that panoramic sphere center,image point and control point are collinear, we obtain the control points' coordinates in spherical panoramic image space; finally, we solve the spherical panoramic image's position and pose directly with the Horn absolute positioning algorithm. Compared with the position and pose estimation method of spherical panoramic image with the resection algorithm, the experiment results show that the method proposed in this paper can achieve higher robustness, higher efficiency without going through an iterative solution, and it can also achieve high accuracy with a few control points.Based on the panoramic camera stitching with non-strict center, the reprojection error can be controlled around 3.00 pixel level.

Geometric calibration of non-metric digital camera, including the accurate determination of interior orientation elements and distortion parameters, directly affects the ultimate accuracy of its using in aerial photogrammetry. In this paper, a laboratory geometric calibration system of non-metric digital camera is developed. A collimator and a star tester are used as the target generator in this system. After measuring locations of targets in CCD and corresponding angles of parallel lights, high precision geometric calibration can be accomplished. Two kinds of mainstream digital aerial cameras, Canon EOS 5D Mark Ⅱ and Hasselblad H3D, are used to take experiments based on this system and the results are analyzed. Experiment results show that the method of this paper is correct and the calibration precision is reliable.

The estimation methods of P-norm distribution is improved in this paper from the perspective of the parameters estimation precision and algorithm complexity. The real order and logarithmic moment estimation is introduced and the real order moment estimation method of P-norm distribution is established based on the actual error distribution. First of all, the relation between the shape parameter p and the real order value r is derived by using the real order moment estimation, and corresponding suggestions are provided for shape parameter's selection. Then, the nonlinear estimation formula of shape parameter, expectations and mean square error is derived via logarithmic moment estimation, function truncation error on the calculation of parameter estimation is eliminated and the solving method of corresponding parameters and calculation process is given, leading an improvement of the theory. Finally, some examples are performed for analyzing the stability and precision of such three methods including real order moment, logarithmic moment and maximum likelihood estimation. The result shows that the stability, precision and convergence speed of the method in this paper are better than maximum likelihood estimation, which generalized the existing errors theory.

The core of modern cadastre management is to renew the cadastre database and keep its currentness,topology consistency and integrity.This paper analyzed the changes and their linkage of various cadastral objects in the update process.Combined object-oriented modeling technique with spatio-temporal objects' evolution express,the paper proposed a cadastral data updating model based on the spatio-temporal process according to people's thought.Change rules based on the spatio-temporal topological relations of evolution cadastral spatio-temporal objects are drafted and further more cascade updating and history back trace of cadastral features,land use and buildings are realized.This model implemented in cadastral management system-ReGIS.Achieved cascade changes are triggered by the direct driving force or perceived external events.The system records spatio-temporal objects' evolution process to facilitate the reconstruction of history,change tracking,analysis and forecasting future changes.

Aiming at the problem of line matching and the effectiveness of matching constraints, this paper presents one new line matching algorithm based on corresponding points and Z-plane constraints. Based on the edge points matching results and the line extraction results, the algorithm firstly determines the candidate lines by corresponding points in neighborhoods of lines, also the elevation values of line projection planes, and then obtains the corresponding lines with the similarity constraint between object space and image space. Secondly, it integrates the matching results of “one-to-many” relationship according to the line indexes and merges those into one line, and then translates the matching results into “one-to-one” relationship. Finally, it determines the corresponding endpoints of corresponding lines by using “image-object-image” mapping mode. In the experiments, this paper adopts digital aerial images with classic texture features for line matching, and the experiment results demonstrate that the proposed algorithm in this paper can obtain reliable line matching results.

Island and coastal image segmentation is of great importance for the subsequent coastline extraction, terrain inversion for intertidal zone, analysis of the situation for shore evolution, and so on. Firstly, the advantages and disadvantages of quadtree, geodesic active contour (GAC) model and Canny edge detector used in the island and coastal image segmentation are analyzed. Secondly, an island and coastal image segmentation method is proposed by integrating quadtree, GAC model and Canny edge detector. The advantages of these three kinds of method are taken in the method proposed in this paper. The method introduces the results of Canny edge detector into edge indicator function of geodesic active contour model based on quadtree segmentation, evolutes the level set equation, and realizes island and coastal image segmentation.The experimental results show that the method proposed in this paper is of high speed, precision, reliability and automation for island and coastal image segmentation, even in the weak edges and serious concave edges.

Filtering is the key to acquire digital terrain model from airborne LiDAR point cloud. In this paper, a new LiDAR point cloud filtering method is proposed. First, the scanning lines formed with point sequence are obtained throuth scanning the point cloud along the same direction. Then a circle with the variable radius rolling over the bottom of these scanning lines, the purpose is to acquire the points on the ground surface and delete the points on the objects at the same time. The next step is interval sampling from the scanning lines. On this basis, after fitting terrain surface with uniform B-spline surface, every point is projected to the fitting surface and calculate its height. According to compare the real height and its projection height to judge every point is on the terrain surface or not. The experiments show that filtering precision of the algorithm proposed in this paper is improved 1 to 5 times of the traditional methods, it can be used for the city, mountains and forest, and the time complexity of the algorithm is O(n).

Existing methods for large regional population density estimation, which are mostly concentrated in the kilometer scale and only reflect the macro distribution characteristics of the urban and rural population, are difficult to describe details of urban and rural population spatial distribution accurately. In order to resolve the problem above, an estimation method of large regional urban and rural population density, which is based on the first 30 m global land cover dataset(GlobeLand30) is proposed. Based on the urban and rural area data partitioned from artificial surfaces data in GlobeLand30 datasets, the population density were estimated in urban and rural area respectively. Urban population density was estimated through the correlation between night lighting intensity and population. Through area revise of rural patches by the method of quadrats estimation, the rural population density was estimated. This paper takes Shandong province as a test area. The result shows that the method of urban-rural population density estimation could reflect the heterogeneity and continuity of the population spatial distribution in urban internal well, and express the population spatial distribution in rural area. By comparison with the reference data, the method of this paper is superior to the reference data in describing the spatial extent of residents and expressing the spatial distribution of population. And due to the globality of GlobeLand30 data, it is feasible to extend the method to a wider area.

In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects which have different sizes; then, according to the features of the objects, the method of change vector analysis is used to obtain the change detection results of various scales. In order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

This paper reveals and utilizes the growing power of online customer reviews in the space and time context. The location of commercial facilities and online customer reviews offered by Dianping.com provide an important data source for the study of spatial and temporal dynamics of urban commercial facilities. The constraints of road network are taken into account towards computing the density of urban commercial facilities and associated online customer reviews, as well as their spatial distribution, temporal trend, and the coupling relationship between facility number and stratification level. This paper maps the spatial distribution of commercial facilities onto the nearby road network, reflecting the influences of the locations, number and satisfaction levels of other commercial facilities across various street types. Because more and more customers tend to make a final shopping decision by sorting through search results by ratings and feedback, the research conducted in this paper can provide the proof for quantitative evaluation of urban planning on commercial facility development.

The 3D crustal movement velocities filed and strain parameters of Guangxi area are acquired in the coordinate frame of ITRF2005 with the multi-period GPS data from 1998 to 2010 analyzed by using GAMIT/GLOBK software, and the area deformation field and present-day construction mechanism are also discussed in this paper. From the GPS monitoring results, the crustal movement of Guangxi area coincides with south China movement in the South-East-East anticlockwise direction, the average vertical velocity value is 33.49 mm/a in ITRF2005 frame, the preponderant direction is N 109.3° E, the average horizon velocity value is +1.30 mm/a, and the trend becomes increasing from East to West of Guangxi province. The present-day strain status is extrusion in N 127.3°±4.8° E direction, the main maximum extrusion strain is -2.7±0.5×10-9/a with a tension effect in the N 37.3°±4.8° E direction, and the main maximum tension strain is 1.8±0.2×10-9/a. A more precise 3D present-day crustal velocity filed of Guangxi area is presented in this paper with the full analyzing of GPS data.

This paper achieves out a new Delaunay triangulation algorithm. Firstly, the self-adaptation grid space division was proposed to realize the balanced logical grid division for massive point cloud data. Secondly, from far to near order the sequence of points in each grid by distance to the grid center and find out the nearest point and mark it as the central point. Thirdly, the TIN was built with by a new general-division Delaunay triangulation algorithm, which uses traditional insertion method to build TIN and add only one point from each grid at one times to form new TIN. When building TIN we use find-insertion method firstly and hereafter use topology-insertion method to keep high efficiency. This algorithm has good efficiency because it successfully avoided the merge process of sub grid triangulation mesh. Finally, the topological closure detection mechanism was established, and the independent parallel multithreading was started to model the rest points by topology-insertion algorithm limit to every grid space, which made the triangulation modeling of the whole space efficient. The method of this paper improved the support capacity of space modeling for massive point cloud data obviously.

A topology adaptive snake (T-Snake) model based on orthogonal grids is introduced and improved in this paper, and a proper energy function is designed. A detection and handling mechanism for topological conflict that caused by island shaped hollow is proposed in the model, and therefore accurate extraction for complex boundary of river containing river islands is achieved. For the disadvantage of the need to manually construct the initial contour in the orthogonal T-Snake model, using the minimum fractal dimension to obtain one area of the water and automatically generate an initial contour. The experiment shows that the algorithm of this paper can accurately extract the boundary of the complex water which contains deeply concave regions or river islands, and it has higher accuracy and less time cost than classic Snake model and GVF Snake model.

When observing the Earth from above at night, it is clear that the human settlement and major economic regions emit glorious light. At cloud-free nights, some remote sensing satellites can record visible radiance source, including city light, fishing boat light and fire, and these nighttime cloud-free images are remotely sensed nighttime light images. Different from daytime remote sensing, nighttime light remote sensing provides a unique perspective on human social activities, thus it has been widely used for spatial data mining of socioeconomic domains. Historically, researches on nighttime light remote sensing mostly focus on urban land cover and urban expansion mapping using DMSP/OLS imagery, but the nighttime light images are not the unique remote sensing source to do these works. Through decades of development of nighttime light product, the nighttime light remote sensing application has been extended to numerous interesting and scientific study domains such as econometrics, poverty estimation, light pollution, fishery and armed conflict. Among the application cases, it is surprising to see the Gross Domestic Production (GDP) data can be corrected using the nighttime light data, and it is interesting to see mechanism of several diseases can be revealed by nighttime light images, while nighttime light are the unique remote sensing source to do the above works. As the nighttime light remote sensing has numerous applications, it is important to summarize the application of nighttime light remote sensing and its data mining fields. This paper introduced major satellite platform and sensors for observing nighttime light at first. Consequently, the paper summarized the progress of nighttime light remote sensing data mining in socioeconomic parameter estimation, urbanization monitoring, important event evaluation, environmental and healthy effects, fishery dynamic mapping, epidemiological research and natural gas flaring monitoring. Finally, future trends of nighttime light remote sensing and its data mining have been proposed from four aspects including new data source, knowledge discovery, in-situ observation, and national/global geographic conditions monitoring.

In order to study SAR land cover classification method, this paper uses the multi-dimensional combination of temporal,polarization and InSAR data. The area covered by space borne data of ALOS PALSAR in Xunke County,Heilongjiang Province was chosen as test site. A land cover classification technique of SVM based on multi-temporal, multi-polarization and InSAR data had been proposed, using the sensitivity to land cover type of multi-temporal, multi-polarization SAR data and InSAR measurements, and combing time series characteristic of backscatter coefficient and correlation coefficient to identify ground objects. The results showed the problem of confusion between forest land and urban construction land can be nicely solved, using the correlation coefficient between HH and HV, and also combing the selected temporal, polarization and InSAR characteristics. The land cover classification result with higher accuracy is gotten using the classification algorithm proposed in this paper.

Uncertainty often exists in the process of obtaining measurement data, which affects the reliability of parameter estimation. This paper establishes a new adjustment model in which uncertainty is incorporated into the function model as a parameter. A new adjustment criterion and its iterative algorithm are given based on uncertainty propagation law in the residual error, in which the maximum possible uncertainty is minimized. This paper also analyzes, with examples, the different adjustment criteria and features of optimal solutions about the least-squares adjustment, the uncertainty adjustment and total least-squares adjustment. Existing error theory is extended with new observational data processing method about uncertainty.

Packing spatial data into blocks and processing of global impact of local operations are two important tasks for spatial index to support bulk operations. In this paper, we present a new spatial index called Pattern-tree for bulk operations with spatial distribution pattern analysis. For packing objects into blocks, a new spatial data partitioning method based on the detection of the spatial distribution pattern was presented. This paper introduces a novel spatial index construction algorithm that combines of top-down and bottom-up methods; For processing of local update operations and its global impact, this paper introduces a new algorithm based on change analysis of the spatial distribution pattern. Empirical results demonstrate that performance improvements are achieved in practice in the case of spatial index construction and windows query compared with STLT, GBI and SCB.

Aiming at problems that in direction relation matrix model, recognition ability for distinguishing direction changes in the same cardinal direction is insufficient, the direction distance references for different cardinal directions are defined incompletely, and the distance calculation between arbitrarily direction relation matrices is not accurate enough, this paper proposes a direction relation two-tuple model, which combines grid-based direction relation matrix and centroid-based direction relation matrix to concern both distribution ratio variations and centroid position variations for objects, thus distinguishing the direction difference in the same cardinal direction. Meanwhile, the traditional neighborhood graph is optimized based on human spatial cognition, and a centroid direction distance reference suitable for arbitrarily direction relationships is established. Finally the Earth mover's distance (EMD) is utilized to further improve the accuracy of distance calculation between direction relation two-tuples. Experiments indicate the method is simple and feasible, the measurement results are more consistent with human cognition, and can be better applied to tasks like cartographic generalization results evaluation.

Shape similarity between buildings is useful for building matching, classification, retrieval, etc. As buildings tend to be designed with square angles, we proposed a method to measure shape similarity for buildings with multiple orthogonal features based on Needleman-Wunsch (NW) and Smith-Waterman (SW) algorithms. While encoding building shape into a sequence, adjacent two angles are taken as codes. Type, angle and length characteristics of the codes are described, and their similarity are then defined. NW and SW algorithms are applied to compute similarities between two encoding sequences of buildings. Shape-based query experiments show that proposed method in this paper can effectively measure the shape similarity between buildings. And the experimental results are also in line with human visual perception.

In urban environments, the GNSS signals could be easily blocked and reflected by buildings, which leads to low positioning accuracy and discontinuity. This paper employs k-means++, Gaussian mixed clustering (GMM) and fuzzy c-means (FCM) clustering methods to separate the line-of-sight signals (LOS), multi-path and non-line-of-sight signals (NLOS). The pseudorange residuals, signal-to-noise ratio, elevation angle, as well as pseudorange rate consistency are considered in the three methods. The performance comparisons of the three methods are carried out by static testing of GPS/BDS integrated system in known position point. The results show that FCM has the greatest silhouette coefficient and the best clustering performance. The positioning performance is therefore been greatly improve by eliminating the NLOS. The root mean squares error (RMSE) of point positioning indicates that positioning results can be improved by 50% after NLOS been eliminated by k-means++ and FCM. Compared with the supervised clustering methods, the unsupervised clustering is easy to implement without prior information, has lower computation burden and has certain advantages in improving the positioning accuracy.

Taxi trajectories not only reflect the static geometrical and topological structures of urban road network, but also carry dynamic navigation information such as turning relationship. Considering that the location, topological connection and turning rules of road intersection are the basis and key to the construction of navigable road network, this paper made full use of the spatial distribution and dynamic connection information of taxi trajectories and proposed a navigable road network generation method guided by intersection information, which adopts the research idea of "intersection location—road segment geometry—navigation attribute". Firstly, an integrated intersection recognition technology considering multi-mode features was designed, and then the random forest algorithm was introduced to explore a zero-label supervised anti-counterfeiting strategy for eliminating false intersections; on this basis, a multi-stage road segment generation method based on Delaunay triangulation network was proposed; finally, the turning relationship and one/two-way information of road segment, which are closely related to intersections, were further captured so as to derive a navigable road network. Compared with other methods, the proposed method achieves higher completeness and accuracy for the extraction of navigable road network information such as road intersection, road segment and turning relationship.

The ZiYuan-3 (ZY-3) surveying satellite is the first civilian high-resolution stereo mapping satellite of China. Its objective is oriented to plot the 1:50,000 and 1:25,000 topographic maps. Comparing with foreign commercial mapping satellite imagery, the establishment of our own imaging geometry model is the core technical problem for different products and various applications of ZY-3 surveying satellite. This paper analyses the key problem on precision geometry processing based on the overall design, and proposes the ZY-3 Surveying satellite imaging geometry model with the technology of virtual CCD line-array imaging. In addition, this paper utilizes the first orbit imagery of ZY-3 satellite with coverage of the region of Dalian, and produces forward, backward and nadir cameras calibration products. Different ground control points are selected for the block adjustment experiment, and the Digital Surface Model (DSM), Digital Ortho Map (DOM) are generated. The accuracy is validated by check points. It can be seen from the experiment that the planar and vertical accuracy are better than 3 meters and 2 meters, respectively. The experiment demonstrates the effectiveness of ZY-3 surveying satellite imaging geometry model

1.1 Objective Map databases are irreplaceable national treasure of immense importance. Their currency referring to its consistency with respect to the real world plays a critical role in its value and applications. The continuous updating of map databases at 1:50,000 scales is a massive and difficult task for larger countries of the size of more than several million’s kilometer squares. This paper presents the research and technological development to support the national map updating at 1:50,000 scales in China, including the development of updating models and methods, production tools and systems for large-scale and rapid updating, as well as the design and implementation of the continuous updating workflow. 1.2 Methodology The updating of map databases is different than its original creation, and a number of new problems should be solved, such as change detection using latest multi-source data, incremental object revision and relation amendment. The methodology of this paper consists of the following three parts: 1) Examine the four key aspects of map database updating and develop basic updating models/methods, such as currentness-oriented integration of multi-resource data, completeness-based incremental change detection in the context of existing datasets, consistency-aware processing of updated data sets, and user-friendly propagation and services of updates. 2) Design and develop specific software tools and packages to support the large-scale updating production with high resolution imagery and large-scale data generalization, such as map generalization, GIS-supported change interpretation from imagery, DEM interpolation, image matching-based orthophoto generation, data control at different levels. 3) Design a national 1:50,000 databases updating strategy and its production workflow, including a full coverage updating pattern characterized by all element topographic data modeling, change detection in all related areas, and whole process data quality controlling, a series of technical production specifications, and a network of updating production units in different geographic places in the country. 1.3 Results A group of updating models and technical methods were proposed after a systematic investigation of the key problems arising from the continuous updating of national 1:50,000 map databases. A set of specific software tools and packages was further developed to support large area updating. With these innovative methodologies and tools, a total of 19,150 map sheets at 1:50,000 scales had been updated and such a massive task was completed in an acceptable time frame, i.e., from 2060-2010. The data currency of national 1:50,000 map databases has been raised from 20-30 years to 5 years! 1.4 Conclusion A modern state requires accurate up to date maps and keeping them up to date on a regular basis is a massive task for a country the size of China. National Geomatics Center of China (NGCC) has solved this problem by using the latest data sources and developing new techniques. The methodologies developed in this paper are suited to regular updating in other rapidly developing nations and establish a model which can be followed in similar circumstances throughout the world.

Environment perception and navigation are the core technologies for automated driving of deep space exploration rovers. Due to the special circumstances of the deep space environment, the rover's environment perception and navigation are particularly challenging comparing to the corresponding technologies in autonomous car driving on Earth. The paper presents a review of the progresses of environment perception, rover localization and path planning, from engineering application and scientific research perspectives. Moreover, the future prospects of intelligent environment perception and long-range rover navigation are discussed.

Vehicle tracking aims at estimating the target vehicle state from continuous temporal measurement. It is the core for intelligent vehicle to understand the environment and predict the targets' behaviors. LiDAR-based perception system of intelligent vehicle provides precise vehicle detection results, which are the basis of vehicle tracking. However, the tracking process suffers from issues of orientation mis-estimation and low stability of tracking trace, especially when the target vehicles are far away from LiDAR. The sparseness of point cloud at long distance is the key problem. Therefore, this paper proposes an enhanced vehicle tracking method based on the lane orientation priori from a digital map. It utilizes the OpenStreetMap digital map to fuse with the local lane markers detection results. The road model is built to obtain the constraint of lane orientation. Based on the vehicle tracking method built on extended Kalman filter, this lane orientation constraint is utilized to improve the vehicle orientation estimation. Consequently, the vehicle tracking accuracy and stability can then be reached. The tracking result indicates the multiple objects tracking accuracy can be increased by 0.33% while the average translation error can be reduced by at least 0.014 m. Moreover, the target vehicle at 60 m away from the host vehicle can be improved to reduce the error of 0.08 m.

Accurately identifying roadside objects like trees, cars, and traffic poles from mobile LiDAR point clouds is of great significance for some applications such as intelligent traffic systems, navigation and location services, autonomous driving, and high precision map. In the paper, we proposed a point-group-view network (PGVNet) to classify the roadside objects into trees, cars, traffic poles, and others, which utilize and fuse the advanced global features of multi-view images and the spatial geometry information of point cloud. To reduce redundant information between similar views and highlight salient view features, the PGVNet model employs a hierarchical view-group-shape architecture to split all views into different groups according to their discriminative level, which uses the pre-trained VGG network as the bone network. In view-group-shape architecture, global-level significant features are further generated from group descriptors with their weights. Moreover, an attention-guided fusion network is used to fuse the global features from multi-view images and local geometric features from point clouds. In particular, the global advanced features from multi-view images are quantified and leveraged as the attention mask to further refine the intrinsic correlation and discriminability of the local geometric features from point clouds, which contributions to recognize the roadside objects. We have evaluated the proposed method on five different mobile LiDAR point cloud data. Five test datasets of different urban scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision, recall, quality and Fscore of trees, cars and traffic poles on the selected testing datasets achieve (99.19%, 94.27%, 93.58%, 96.63%), (94.20%, 97.56%, 92.02%, 95.68%), (91.48%, 98.61%, 90.39%, 94.87%), respectively. Experimental results and comparisons with state-of-the-art methods demonstrate that the PGVNet model is available to effectively identify roadside objects from the mobile LiDAR point cloud, which can provide data support for elements construction and vectorization in high precision map applications.

Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots.

The floating-car GNSS trajectory data have been widely used to obtain and predict the urban traffic status in real time, with wide coverage and low deployment cost, and the result has an important supporting role for route decision-making of automatic driving and traffic management. However, the traffic information predicted by the floating-car GNSS data only contains the traffic information on each road segment, ignoring the difference of the traffic flow in different driving directions at the intersection; besides, the accuracy of the traffic information is limited by the GNSS sampling frequency. This paper proposes a turning-level traffic prediction method based on graph convolutional network and low-frequency GNSS trajectory data: first, a queuing-starting-point estimation model is proposed considering vehicle movement pattern; second, a Graph-structure of the turning connection relationship is constructed based on the dual graph theory; finally, to consider spatio-temporal pattern, a traffic prediction model is constructed based on the graph convolutional network. The experimental results show that our method can accurately obtain and predict the traffic speed and queue length at turning-level, and effectively improve the accuracy of traffic prediction by learning the spatio-temporal pattern within the Graph.

HD (high definition) maps have gradually become an indispensable part of autonomous driving, but there is no unified data model and standardize way of description, especially in the map production and data exchange stages. In response to this problem, the paper first analyzes the advantages and disadvantages of the current HD map data models such as NDS, OpenDRIVE and LaneLet, and then proposes a generalized data model for HD maps called Whu map model. In terms of the lane model, lane groups are used as data management units, which composed of one or several lanes on the same road section. And the lane is composed of the left lane divider, the right lane divider, the lane centerline, and lane attributes. Through the lane topology construction on some scenes where lane number changes, the validity and robustness of the lane model are verified. In terms of the data model of traffic facilities, the road marking model and traffic sign model are defined. The geometry, type, semantic information, and association with roads and lanes are the main contents described. Finally, through large-scale data production, compilation to NDS and OpenDRIVE format, and electronic horizon application experiments based on the ADASIS V3 protocol, the usefulness and effectiveness of the data model are verified. Whu map model can be used as a general exchange format, and can also be applied to all stages of HD map production. At the same time, it is backward compatible and easy to be extended. All of these help to realize the standardization of the HD map data model, thereby promoting the large-scale production and application of HD maps.

To improve the positioning accuracy of mobile robot, a visual SLAM algorithm based on binocular vision and inertial navigation is presented. In the front part of visual SLAM, a semi-direct binocular visual odometer combining direct method with characteristic method is presented to maintain the fast calculation speed and high accuracy of direct method. In the back-end optimization stage, the visual data and IMU data are fused together, and error functions are constructed in a sliding window in a non-linear optimization way to optimize the accuracy of pose calculation. The algorithm proposed in this paper is validated in EuRoc dataset. The results show that the positioning accuracy of the SLAM system OKVIS, ROVIO and VINS-Mono is significantly improved in both Machine Hall and Vicon Room scenarios, while maintaining high operational efficiency, compared with the open source visual inertial navigation fusion SLAM system OKVIS, ROVIO and VINS-Mono.

Given the steepness of road slope in surface mines, one unmanned truck may run at risk in unknown environment if it can't plan a proper speed in advance. Therefore, it is crucial for an autonomous vehicle to perceive an accurate value of road slope of it in real time. However, the accuracy is hard to achieve in existing methods, including global navigation satellite system (GNSS), inertial navigation system (INS) and simultaneous localization and mapping (SLAM). For GNSS or INS, they can measure a truck's angle of pitch, but this angle cannot be equal to road slope due to its large pitching and bouncing movements caused by steep and uneven roads. For the same reason, SLAM doesn't work well either. Also, it will lose efficacy as geometric features are not obvious in open-pit mine. To deal with these challenges, this paper proposes a grid Kalman road slope real-time detection (GKSRD) method. The method's input is 3D point cloud of lidar and the pitch of INS. And the method uses a 2D grid map, an iterative optimization algorithm in rectangular region of interest and the Kalman filter. Compared with methods based on GNSS or INS, this method minimizes the error of slope detection. Different from methods based on SLAM, this method does not rely on geometric features of the environment. Verified by experiments, the average error of the road slope detected by GKSRD method is less than 0.01 degree, and the maximum error is less than 0.5 degree. Hence, compared with methods based on INS or GNSS and methods based on SLAM, GKSRD method is more accurate, stable and adaptive.

Cameras can help to make low-cost positioning and environment perceiving in autopilot but dynamic objects give negative effects to visual odometry. This paper gives a model to optimize it based on semantic elements association. It uses such techniques as objects detection and semantic segmentation to identify semantic objects and distinguish dynamic semantic elements(DSE) from static ones. Then it filters bad keypoints by a dynamic feature mask in visual positioning. In practice, this proposed method detects DSE even when there are objects with duality of moving and static. In an experiment on campus roads, negative influences were found especially when a robot turning around or a moving object crossing its camera view. It showed that average accuracy in detecting DSE was 87% and the largest difference between trajectories before and after semantic association optimizing in sub-sequences was 2.463 m. Compared to ground truth, the RMSE of proposed method dropped by 38%.

To solve the problem of matching difficulty caused by the significant nonlinear grayscale differences between SAR and optical images, this paper proposes a fast matching algorithm based on image structural properties named SOFM(SAR-to-optical fast matching algorithm).The traditional methods based on image grayscale are generally difficult to resist the nonlinear grayscale differences between SAR and optical images, but the geometric constructs and shape features can exist stably among different types of images, so in our the proposed method both the magnitude and orientation information of image gradient are used to build a geometric structural feature descriptor named AWOG(angular weighted orientated gradients), then based on the template matching strategy, the sum of squared difference of the descriptors is used to define the similarity metric for matching and then the image matching function expressed in the frequency domain is given. A complete set of image matching process is established based on SOFM, and has been validated using multiple pairs of SAR and optical images, the results show that the proposed method can effectively resist the nonlinear grayscale differences between SAR and optical images, and outperforms the traditional classical image grayscale-based methods and existing image structural-based methods in matching performance and precision.

In order to solve the problem that the parameters of pulse-coupled neural network can't be adjusted adaptively in pan-sharpening image fusion, a non-subsampled shearlet transform remote sensing image fusion method based on the combination of parametric-adaptive pulse coupled neural network model and energy-attributing fusion strategy is proposed. First, the high and low frequency coefficients are obtained by extracting the Y luminance component of the multispectral image YUV color space transform and transforming it with the panchromatic image. Then, aiming at the low-frequency sub-band coefficients are fused by the EA method, the high-frequency sub-band coefficients are obtained by the PA-PCNN model to determine the optimal PCNN model, and then the high-frequency sub-band coefficients are fused; finally, the fusion image is obtained by inverse transformation of NSST and YUV. In this paper, six objective quality indexes, such as spatial frequency, relative dimensionless global error, ERGAS, correlation coefficient, visual information fidelity for fusion, gradient-based fusion performance and structural similarity index, are selected to evaluate the spectral and spatial detail information of the fused images, compared with SE, DGIF, COF and PA-PCNN fusion methods, the proposed method is validated by using multiple sets of high-and low-resolution panchromatic and multispectral remote sensing images, the results show that this method is generally superior to the traditional fusion method of panchromatic and multispectral remote sensing images in objective evaluation and visual perception.

GF-7 satellite is equipped with the first full waveform laser altimeter of China for earth observation, which is mainly used to obtain sparse elevation control points on the ground, and ultimately improve the stereo mapping accuracy of stereo images without ground control points. The laser altimetry standardization processing of GF-7 satellite is the key step of surveying and mapping application, and the generated laser altimetry standard products are the important premise of subsequent distribution and application. Based on the GF-7 satellite laser altimetry data, this paper studies the method of laser altimetry data processing, and preliminarily validates the geometric accuracy of laser altimetry standard products. Three regions are selected to validate the accuracy of GF-7 satellite laser altimetry standard products, which contain the domestic geometric calibration area, Huayin of Shaanxi province, North Rhine-Westphalia of Germany. The results show that the planimetric accuracy of laser altimetry standard products SLA03 in calibration area is (3.896±1.029)m and (3.286±0.337)m, and elevation accuracy is (0.018±0.099)m,(-0.017±0.096)m in respectively for the two-beam lasers. Moreover, the elevation control point quality control parameter ECP_Flag can effectively identify the laser points that can be used for elevation control. The overall accuracy of Shaanxi Huayin area is (-0.113±2.519)m and (0.191±1.071)m, respectively for the two beams. The elevation accuracy of laser points marked with ECP_Flag=1 is (0.111±0.152)m and (-0.064±0.115)m. The overall accuracy of Norwich in Germany is (-0.897±5.485)m and (-0.202 ±6.207)m, and the elevation accuracy of laser points marked with ECP_Flag=1 is (-0.304±0.190)m and (-0.279±0.220)m, respectively for the two beams. At present, the laser altimetry standard products of GF-7 satellite have been daily produced in the Land Satellite Application Center of the Ministry of Natural Resources.

In order to improve the accuracy of pixel-level change detection methods, this paper proposes a novel change detection method for remote sensing images by combining neighborhood information (including the neighborhood correlation image (NCI) and matching errors) and structural features. First, a technique of neighborhood correlation analysis is used to obtain the NCI which represents the context information, and the cross-correlation of neighborhood pixels is used to obtain matching errors by a template matching scheme. Then, structure features of images are extracted using orientated gradient information, which are robust to spectral differences between images. Subsequently, the initial change detection results is obtained by using the NCI, the matching errors, and structural features as the classification attributes of a decision tree. Finally, the Markov Random Field (MRF) is used to optimize the results, yielding the final binary map. The proposed method has been evaluated with two sets of bi-temporal remote sensing images from different sensors. Experimental results demonstrate that this method effectively improves the accuracy of change detection compared with the change vector analysis method, the single neighborhood information method and the method combining neighborhood information and texture features.