Table of Content

25 April 2012, Volume 41 Issue 2

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学术论文

Construction of Gravity Anamoly Degree Variance Model and Application in Computation of Spetral Sensitivity of Disturbing Gravity Functions

¹, ¹, ²

2012, 41(2):  159-164. 

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The analysis of classical gravity anomaly degree variance model is made according to the new gravity field model-EGM2008, the results show that the traditional gravity anomaly degree variance model can’t exactly describe the spectral sensitivity of disturbing gravity funtions. Based on the Moritz model ,a new high degree variance model-TSD model is presented under two spectral sector, the standard deviations and mean value of results comparison with EGM2008 are respectivelly 0.25,0.0 , .The spectral sensitivity of geoid undulation,gravity anomaly,disturbing gravity,deflection of vertical are computed by using the TSD model.The results show that the spectral sensitivity of gravity anomaly,disturbing gravity and vertical deflections increase largely in medium and low spectra.At the same time,the ratio of both three disturbing gravity functions in high spectra decrease largely.

Effect Analysis of System-Dependent Center-of-Mass Correction on Precision of SLR Orbit Determination

2012, 41(2):  165-170. 

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Center-of-Mass (CoM) correction is a kind of systematic bias which must be corrected in precise orbit determination (POD) using satellite laser ranging (SLR) data. Numerical simulation and theoretical analysis have demonstrated the dependence of CoM corrections on system operating modes of SLR stations due to satellite signature effect, i.e. different CoM corrections should be applied on SLR data from different stations. For the first time, we analyze the influence on POD exerted by such dependence. Statistics over long-time series showed after adopting system-dependent CoM correction, the short-arc orbit determination precision has indeed undergone general improvement comparing to the situation of traditional global uniform CoM correction. The mean precision improvement is approximately 0.4 mm for Lageos-1/2 and 0.6 mm for Etalon-1/2. As the current requirements on SLR data processing for relevant applications have achieved sub-centimeter even towards millimeter level, it is necessary to take in the effect of system-dependent CoM correction.

Gravity Changes Observed by GRACE before the Japan Mw 9.0 Earthquake

2012, 41(2):  171-176. 

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The decorrelation and Gaussian smoothing method were was implemented in GRACE monthly gravity field products to obtain annual and seasonal gravity changes in Japan and its vicinity, as well as time series gravity changes of several points. Results showed regional gravity fields in Japan and its vicinity varied from positively to negatively and from area to area in five years before the Japan MW9.0 earthquake, ; obviously positive to negative gravity changes were formed in 1-2 years before the Japan MW9.0 earthquake; , and tTime series gravity changes of several points indicated the Japan earthquake displayed the similar time variation gravity phenomenon with the 1976 Tangshan earthquake; . These results reflected mass migration, mass movement and energy accumulation in Japan and its vicinity before the earthquake occurred, which provided evidence for the earthquake inoculation process research.

Dynamic 3D Model of Complex Air-ground Environment for Visual Aircrafts Navigation

2012, 41(2):  177-183,. 

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A method for controlling shoal-bias based on uncertainty

2012, 41(2):  184-190. 

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Aiming at the shortage of a common experiential shoal-biased method assuring navigation safe, a method for controlling shoal-bias based on uncertainty is proposed. Uncertainties of raw depth and interpolated depths are calculated, a safe and reliable model for controlling shoal-bias with a quantitative value based a depth uncertainty is designed, and the probability of two depth models assuring navigation safe are compared and analyzed. Experimental results demonstrate: The proposed method is much more reliable in security than that of the experiential shoal-biased binning method, especially in areas where depths change complexly.

Triple Linear-array Imaging Geometry Model of ZiYuan-3 Surveying Satellite and its Validation

2012, 41(2):  191-198. 

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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

An Improvement of Minimize Local Maximum Algorithm on Searching Seam Line for Orthoimage Mosaicking

2012, 41(2):  199-204. 

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Orthoimage has relief displacements in image edge and the area with buildings or trees. And displacements display differently on different orthoimages. An ideal seam-line should steer clear of high-displacement-area. This paper proposes an approach of advanced Minimizing Local Maximum in searching seam-line on differential image of overlapped region between mosaicked orthoimages. The experimental results have shown that this method is self-adaptive, and is able to select a seam-line which can steers clear of high-displacement-area, to achieve seamless mosaic.

UNMIXING OF HYPERSPECTRAL MIXTURE PIXELS BASED ON SPECTRAL MULTISCALE SEGEMETED FEATURES

2012, 41(2):  205-212. 

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One of the most important points to improve abundance estimation for linear mixture spectral model lies in end-member spectral constituent. A novel approach to improve abundance estimation of hyper-spectral image using spectral piecewise constant features is presented. This method firstly extracts the spectral features by partitioning the spectral signals into a fixed number of contiguous intervals with constant intensities in terms of minimizing the mean square error. Then, the estimation is performed by unmixing the pixel in the feature space with constrained least square algorithm to achieve the respective abundance fractions of these end-members present in the pixel. Algorithm validation and comparison were done with simulated and real data. Experimental results demonstrate the proposed method can significantly improve the least squares estimation of end-member abundances using remotely sensed hyper-spectral signals, as compared to those of original hyper-spectral signals or discrete wavelet transform based features.

Remote Sensing Classification Based on Markov Random Field and Fuzzy C-means Clustering

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2012, 41(2):  213-218. 

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Fuzzy C means clustering is a classic non-supervised clustering model, successfully applied to remote sensing classification. However, the method is the sensitivity to the initial values selected randomly, easy to fall into a local optimal solution; also uses only spectral information and ignores spatial information. This paper presents a new clustering algorithm integrates with Fuzzy C-means clustering and Markov random field. The density function of the first principal component sufficiently reflects the class differences, from which the initial label for FCM algorithm can be efficiently determined, and the sensitivity of the initial value selected at random can be avoided. Meanwhile, this algorithm takes into account the spatial location information between pixels. The experiment shows that the proposed method is better than the general FCM algorithm.

Filtering of Airborn LiDAR Point Cloud Data Based on Car(p,q)-Model and Mathematical Morphology

2012, 41(2):  219-224. 

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Based on the existing post-processing methods of LiDAR data, this paper proposes a new“separated step-by-step”filtering method of point cloud. First, a“rough”filtering method is applied to the LiDAR point cloud and the“ground points hypothesis”and“non-ground points hypothesis”are gained. Then, a causal auto-regressive model (car-model) is imported to do modeling of the ground surface and hypothesis test for the two classes of point clouds, and ground points and non-ground points are classified by the results of the hypothesis testing. Finally, a reliable classification results is gained. Compared to the“Least-Squares Prediction Method”and“mathematical morphology”, the results of LiDAR point cloud filtering by the“separated step-by-step”processing method is more reliable.

The Removal of Thick Cloud and Cloud Shadow of Remote Sensing Image Based on Support Vector Machine

2012, 41(2):  225-231,. 

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This paper suggests a removing approach of thick cloud and cloud shadow in remote sensing image based on support vector machine. Firstly, the learning ability of support vector machine is used to detect cloud in remote sensing image, and combining the information of solar angle, cloud shadow area is detected. So the binary images of cloud and its shadow are obtained. Secondly, the remote sensing images are transformed by support vector value contourlet transform. The transforming cofficients are mosaiced using selection matrices produced by the binary images to achieved preliminary removal of cloud and its shadow. The cloud and its shadow which can not be removed by image mosaic are repaired by using the method of statistics. Finally, thick cloud and its shadow are removed by reconstructing image and using median filter. Experiments show that the method proposed in this paper can better recover the ground information covered by cloud and the cloud removal image have better image smoothness and image definition.

Restoration of Irregular Sampled Remote Sensing Image

2012, 41(2):  232-238. 

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This paper proposes an image restoration algorithm for remote sensing image to eliminate irregular sampling effect. The algorithm combines the ACT algorithm and the total variation to eliminate several degradation artifacts, and integrates the nonlocal means operator to propose a remote sensing image restoration model based on nonlocal total variation to eliminate irregular sampling effect, then uses operator splitting method and extended Chambolle’s projection algorithm to solve the model. Experimental results show that the proposed algorithm can reduce the staircase effect effectively and improve the detail information of the restored image.

A Technique of Decision at the Region Level Based on Region of Interest for Change Detection in SAR Images

2012, 41(2):  239-245. 

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In the context of change detection in SAR images, most of techniques are based on the analysis of the difference image, while almost all make the decision at the pixel level. This decision way will cause the change detection map noisy, with holes in connected component and jagged boundaries. For avoiding this, a technique making the decision at the region level is proposed, whose core is extracting and handling region of interest (ROI) and the key is obtaining the proper the label used to guide for extracting ROI and that how to make the result generated at the region level. To make the extracted ROI contain nearly all pixels in the changed area, stationary wavelet transform (SWT) and fuzzy c-means (FCM) algorithm have been used to get it; to make the change detection map generated at the region level, all connected regions in the extracted ROI are searched and each one is looked at as a unit to be handled. Then the threshold-based method is used to group those units into two classes. Results tested on the real SAR datasets have shown that our method outperforms other related techniques, whatever from quantitative or subjective aspects.

A High-quality Filtering Method with Adaptive TIN Models for Urban LiDAR Points Based on Priori-knowledge

2012, 41(2):  246-251. 

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According to the characteristics of urban LiDAR point clouds, we proposed a knowledge-based filtering algorithm with adaptive TIN models. The main strategies are: ① taking object-oriented segmentation for raster data interpolated regularly; ② separating terrain objects from off-terrain objects by using iteration Otsu clustering method; ③ constructing the initial TIN form classification results and adjusting the parameters of the ground point criterion adaptively in the aim of improving the filtering quality. We did experiment with the real data of ALS50 system and also assessed the results quality with traditional algorithm. The result shows that knowledge-based filtering method can further improve the quality of point clouds filtering.

3D Modeling Approach of City Cloverleaf from Airborne LiDAR Data

2012, 41(2):  252-258. 

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The cloverleaf is an important component of urban traffic planning, at the same time it is one of the most complex features in the city. The 3D visualization problem of cloverleaf is a serious topic in cyber-city construction. To the reason of its complexity in construction and topology, results made by many traditional unified modeling functions are not able to reflect its spatial significance, while the abstract expressions of some commercial software cannot provide intuitional investigation and interactive operation. To solve these problems, this paper presents a method to build 3D visualization model of city cloverleaf based on airborne laser scanner data. The prime features include: 1) the method enters the modeling step after the segmentation of cloverleaf datasets. We provides a automatic segmentation algorithm which divide the complex structure of road divaricates and cross into sections of simple construction. This operation laid a solid foundation for the next work; 2) we use all-constraint triangulation algorithm to build the model of cloverleaf, the constraint section is chosen from the contour; 3) we can detect and reconstruct the absent part of the data occluded by the upper structure, it is based on the model features of sections combines with priori knowledge. The results shows our function which build the 3D model after the segmentation process can worked out satisfactorily, the all-constraint triangulation algorithm keep well of the spatial characters of cloverleaf, more importantly, it can help to rebuild the part occluded by the upper structure. The analysis further explains the feasibility and efficiency of our method.

Accumulated Similarity Surface for Spatial Weights Matrix Construction

2012, 41(2):  259-265,. 

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Spatial weights matrix is used to represent geographical feature similarity. In situ similarity is represented by different kinds of distance measurements based on Euclidean distance. This kind of similarity builds on spatial dependence but neglects spatial nonstationarity. In this paper, geographical feature similarity is defined as attribute and spatial similarity. We propose the concept of accumulated similarity surface and bring in curve evolution and fast marching method to calculate the accumulated similarity surfaces of geographical features. Spatial weights matrix is constructed using accumulated similarity surfaces according to both spatial dependence and spatial nonstationarity. Experiments are processed using trend surface and ASTER DEM as experimental data. The results show that spatial weights matrix based on accumulated similarity surfaces performs better than Euclidean-distance-based spatial weights matrix.

A Robust Texture Image Registration Method for Terrestrial LiDAR Data

2012, 41(2):  266-272. 

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In order to get true and high resolution texture for terrestrial LiDAR point clouds, the photo should be taken to all directions. This will cause the large angle problem for the registration of point clouds and the texture image. Meanwhile, the error from control points and scanner also creates the low registration accuracy. A robust registration method is proposed to solve these problems. First, rotation element model of registration parameter is derived by the barycenter model of the space similarity transformation, normal rotation matrix and the antisymmetric matrix. The registration result is treated as initial value. Then, the high accuracy registration parameters are obtained by improved Danish iteration method with variable weights based on collinearity equation. Experiment shows that new method is robust and accurate for the registration of any angle image and point clouds.

Stochastic Process Based Accuracy Field Model for Grid DEM

2012, 41(2):  273-277. 

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Digital Elevation Model (DEM) is known as the important GIS foundational data used in geo-simulation and terrain analysis, and its accuracy especially local accuracy have gained widespread research ever since DEM concept was proposed in 1958. This paper proposed an accuracy field model based on stochastic process method to estimate the DEM grid accuracy. Compared with traditional models, the model proved two kinds of DEM errors (noise error and approximate error) can be evaluated with uniform mathematical description, which further revealed the mathematic meaning for each components that made up of DEM error; As to application aspect，the model has sound versatility that can adapt to all error assessing on condition that build grid DEM using interpolation method.

A New Method for Extracting Curved-polygon Medial Axis

2012, 41(2):  278-283,. 

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It is difficult for existed polygon medial axis (MA) extracting methods to address complex polygon and to make sure of the accuracy and connectivity of MA. The objective of this study is to introduce a new curved-polygon MA extracting method that is based on the mean distance transformation (MDT) of the nearest border point set (NBPS). This paper follows four steps. Firstly, on the base of simple polygon, concepts of curved-polygon together with its MA are given. Secondly, MDT of NBPS，that will be used to adjust distance values for eliminating the impact of noisy points is put forward through extending raster distance transformation (DT) method. Thirdly, efficient judgment regulations which are constructed according to elements of DT and characteristics of MA points, and the method of greed points-growth and detection are used to complete the mission of extracting MA. Furthermore, specific steps and processes to achieve extracting MA are given. Fourthly, various complex polygons are used to inspect this new method. It is found that the impacts of border noisy points are eliminated effectively and both the accuracy and connectivity of MA are satisfactory which overcome the shortcomings of traditional methods.

The Progressive Transmission Model for Contour based on Fourier Series

2012, 41(2):  284-290. 

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The key technology of progressive transmission for vector map is the successive multiple representations of spatial data to form the map data to line-structure organization in the server. The paper aims at vector contour data offering a new progressive transmission model based on Fourier series which has the properties of multiple representations. The corresponding relation between map scale and the number of Fourier series expanded-item is discussed, and the Fourier descriptor model for curve with multi-scale representation can be built with the relationship on the server site. The Fourier descriptor components for different map scale will be available gradually and curves will be rebuilt with them on the browser site, so the progressive transmission and representation for contours can be achieved. The actual application of model has two main characteristics. First, what is transmitted in the network is feature vectors extracted from curves, which played a important role in data compression, rather than coordinate data of curves; second, continuous map scale representation can be achieved with the model.

Application of Rough Set in Attribute Table without Decision during Map Generalization

2012, 41(2):  298-301,. 

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On the basis of the feature of spatial information of map and the particularity of its decision table when apply rough set theory and method to map generalization, an idea of discretize the continuous conditional attributes in the decision table by the method of discretizaton with nonsupervision before adding decision attribute according to the vague categories of spatial objects which obtained by the application purpose of decision table is proposed. Then calculate the importance of the conditional attributes by the method of attribute reduction of rough set to access the importance degree of spatial objects with dynamic sequencing. This process can resolve the problem of can not determine the decision attribute inadvance when apply rough set theory to map generalization, and the experiment result of spatial points selection shows its rationality and validity.

Study on the Model of Light and Shadow in 3D Map Based on Spatial Cognition

2012, 41(2):  302-308. 

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There have many disadvantages about the expression of light and shadow in the existing 3D map. For example, it lacks variation of representation technique, it is short of adjustment function and has only a limited cognitive effect. To improve this situation, this paper discusses the change rules of the color of light and shadow along with latitude, longitude and time in terms of Chromatology. The author takes the people's spatial cognition custom fully into account to build a simple and feasible model of light and shadow in 3D map. This model can simulate the spatial-temporal variation processes and rules conveniently and realistically. It can also provide a basis for using the existing 3D modeling software or developing the visualization software of 3D map by component technology, and strengthen the visual effect of 3D map.

Study of Auto-vectorization based on Scan-thinning-algorithm

2012, 41(2):  309-314. 

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The auto-vectorization of raster map is the important way of the collection of geographic information. But at present, the result of the main software for auto-vectorization is bad with some aspects which make the great computation of disposing the vector lines. It’s designed the Scan-thinning-algorithm based on the morphological characteristics of the raster lines. This algorithm though 3 steps to thinning the raster lines, first, tracking the undetermined pixel by pushing with the sequential of the line; the second, obtaining the Center-pixel by scanning truncation of the line; finally, connection the present Center-pixel and the last Center-pixel with the ordinary Thinning-pixel. The result of the Scan-thinning-algorithm has some characteristics, less influence of the edge conditions, less Center-pixels, and grate precision. It’s designed the auto-tracking-vectorization algorithm dispose the result of the Scan-thinning-algorithm. By the way, reduced the working of coordinate transfer, at the same time the result of this algorithm has some characteristics, less nodes, the smooth result approach with the center of line, do not mutual interrupted at the intersection, so that , which greatly improved the efficiency of auto-vectorization and post-processing. Finally, this paper introduces the auto-vectorization and the post-processing method of the raster area map.

博士论文摘要

Theory and Methods of the Earth’s Gravity Field Model Recovery from GOCE Data

2012, 41(2):  315-315. 

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Applications of Shape Recognition in Map Generalization

2012, 41(2):  316-316. 

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As an important characteristic of spatial cognition and a cognition result of graphic structure, pattern and distributing condition, shape plays a significant role in map generalization. Structural recognition of geographical features is essential in map generalization, which includes the analysis in distributing pattern and shape characteristic. Therefore, map generalization overall strategy could be established on the basis of the acquired distributing rule of geographical features and phenomena.Secondly, whereas a feature should be kept shape similarity in multi-scale representation, shape fidelity takes priority in generalization algorithm. Thirdly, shape similarity is an important index in map generalization evaluation. As an aspect in the field of cognition, shape recognition carries subjectivity and uncertainty. Therefore, shape analysis and recognition in map generalization is challanging and practical as well.