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Cui S.,German Aerospace Center | Yan Q.,Chinese Academy of Surveying and Mapping | Reinartz P.,German Aerospace Center
Remote Sensing Letters | Year: 2012

A simple but robust approach for complex building description and extraction from high-resolution remotely sensed imagery based on graph-based shape representation is proposed. Classical approaches for building extraction usually involve a complex grouping process of low-level primitive features and are not robust in the presence of noise. To overcome these drawbacks, this approach presents an efficient and robust solution by integrating edges and regions. First, a region segmentation method is applied to obtain the approximate shape of the building. Second, Hough transformation is employed to derive the two perpendicular line sets corresponding to the building boundary. Third, a subset of the intersectional nodes of the two line sets is utilized to construct a building structural graph, based on the analysis of grey value difference between the two sides of each line segment. Finally, a graph search algorithm is performed to retrieve all the cycles in the structural graph. The cycle corresponding to the building boundary is identified as the final building outline on the basis of its area. Two experiments were carried out to evaluate and validate this approach and experimental results confirm its effectiveness and robustness. © 2012 Taylor & Francis.

Jiang T.,Chinese Academy of Surveying and Mapping | Wang Y.M.,National Oceanic and Atmospheric Administration
Journal of Geodesy | Year: 2016

One of the challenges for geoid determination is the combination of heterogeneous gravity data. Because of the distinctive spectral content of different data sets, spectral combination is a suitable candidate for its solution. The key to have a successful combination is to determine the proper spectral weights, or the error degree variances of each data set. In this paper, the error degree variances of terrestrial and airborne gravity data at low degrees are estimated by the aid of a satellite gravity model using harmonic analysis. For higher degrees, the error covariances are estimated from local gravity data first, and then used to compute the error degree variances. The white and colored noise models are also used to estimate the error degree variances of local gravity data for comparisons. Based on the error degree variances, the spectral weights of satellite gravity models, terrestrial and airborne gravity data are determined and applied for geoid computation in Texas area. The computed gravimetric geoid models are tested against an independent, highly accurate geoid profile of the Geoid Slope Validation Survey 2011 (GSVS11). The geoid computed by combining satellite gravity model GOCO03S and terrestrial (land and DTU13 altimetric) gravity data agrees with GSVS11 to ±1.1 cm in terms of standard deviation along a line of 325 km. After incorporating the airborne gravity data collected at 11 km altitude, the standard deviation is reduced to ±0.8 cm. Numerical tests demonstrate the feasibility of spectral combination in geoid computation and the contribution of airborne gravity in an area of high quality terrestrial gravity data. Using the GSVS11 data and the spectral combination, the degree of correctness of the error spectra and the quality of satellite gravity models can also be revealed. © 2016 Springer-Verlag Berlin Heidelberg

Zhang J.,Chinese Academy of Surveying and Mapping | Lin X.,Chinese Academy of Surveying and Mapping | Ning X.,Chinese Academy of Surveying and Mapping
Remote Sensing | Year: 2013

Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications. © 2013 by the authors.

Niu R.C.,Chinese Academy of Surveying and Mapping
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015

Although some progresses have been made after nearly 30 years of research and practice of administrative division renaming in China, there are still many problems which have not been really resolved. Moreover, with the beginning of the second place name census across China, scientific and standard administrative division naming countermeasures are of even more importance. In this paper, the problems in present administrative division naming were analyzed, and the basic characters and principles of traditional Chinese noun naming as well as five taboos in Chinese place naming were described by analyzing the theory of Chinese naming concept. Based on the characters above, principles of administrative division renaming and naming conceptions of administrative division were further discussed and analyzed.

Zhang J.,Chinese Academy of Surveying and Mapping | Lin X.,Chinese Academy of Surveying and Mapping
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2013

Progressive TIN densification (PTD) is one of the classic methods for filtering airborne LiDAR point clouds. However, it may fail to preserve ground measurements in areas with steep terrain. A method is proposed to improve the PTD using a point cloud segmentation method, namely segmentation using smoothness constraint (SUSC). The classic PTD has two core steps. The first is selecting seed points and constructing the initial TIN. The second is an iterative densification of the TIN. Our main improvement is embedding the SUSC between these two steps. Specifically, after selecting the lowest points in each grid cell as initial ground seed points, SUSC is employed to expand the set of ground seed points as many as possible, as this can identify more ground seed points for the subsequent densification of the TIN-based terrain model. Seven datasets of ISPRS Working Group III/3 are utilized to test our proposed algorithm and the classic PTD. Experimental results suggest that, compared with the PTD, the proposed method is capable of preserving discontinuities of landscapes and reducing the omission errors and total errors by approximately 10% and 6% respectively, which would significantly decrease the cost of the manual operation required for correcting the result in post-processing. © 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Qiao Q.,Chinese Academy of Surveying and Mapping
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2014

Since the proposal of Digital Earth, its research and applications are continuing to be deepened, and now Smart City is more in-depth implementation of the Digital Earth. The unification of global or regional vertical datums has always been one of the main geodesy studies to achieve Smart City, as Smart City must first realize the seamless integration of multi-source geo-dataset. This paper introduces spatio-temporal data management and interactive visualization into the entire process of vertical datum modelling. We demonstrate that the efficiency of modelling is greatly improved. In this paper GIS database is used to manage all dataset such as tidal station data, gravity field model data, GPS leveling data, and vertical datum data for evaluation and analysis. We use geographic information visualization technique to graphically display the results, together with the interactive browsing, to convert tedious digital information to easy-to-understand images. Consequently, researchers can quickly and comprehensively grasp the macro and micro information. Finally, an efficient and interactive prototype operating platforms for vertical datum modeling is constructed based on GIS.

Lin X.,Chinese Academy of Surveying and Mapping | Zhang J.,Chinese Academy of Surveying and Mapping
Remote Sensing | Year: 2014

Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.©2014 by the authors; licensee MDPI, Basel, Switzerland.

Zhang J.,Chinese Academy of Surveying and Mapping
International Journal of Image and Data Fusion | Year: 2010

With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fusion methods have mainly focused on optical data fusion; high-level fusion includes feature level and decision level fusion of multi-source data, such as synthetic aperture radar, optical images, LiDAR and other types of data. Finally, this article summarises several trends tending to broaden the application of multi-source data fusion. © 2010 Taylor & Francis.

Dang Y.,Chinese Academy of Surveying and Mapping | Xue S.,Chinese Academy of Surveying and Mapping
International Association of Geodesy Symposia | Year: 2014

Newton-type methods were ordinarily developed by using approximate Hessian matrices to solve the nonlinear equations. For dealing with ill-posed ranging resection problems, we propose a new Newton-type iterative formula based on the precise Hessian matrix, called closed-form Newton iterative formula. An orthogonal condition is introduced to the nonlinear least squares solution of an over-determined distance equation system. It is revealed that the solution is the barycentre of a particle system composed of unit masses at the endpoints of the ranging vectors. Then a closed-form of the Newton method is given by compactly expressing the Hessian matrix. The simulation result shows that the closed-form Newton method can improve the efficiency and the stability of the convergence, especially in the case of the ill-posed positioning configurations. © Springer-Verlag Berlin Heidelberg 2014.

Xue S.,Chinese Academy of Surveying and Mapping | Xue S.,Chang'an University | Yang Y.,Xian Research Institute of Surveying and Mapping | Dang Y.,Chinese Academy of Surveying and Mapping
Journal of Geodesy | Year: 2014

The Newton method has been widely used for solving nonlinear least-squares problem. In geodetic adjustment, one would prefer to use the Gauss-Newton method because of the parallel with linear least-squares problem. However, it is proved in theory as well as in practice that the Gauss-Newton method has slow convergence rate and low success rate. In this paper, the over-determined pseudo-distance equations are solved by nonlinear methods. At first, the convergence of decent methods is discussed after introducing the conditional equation of nonlinear least squares. Then, a compacted form of the Hessian matrix from the second partial derivates of the pseudo-distance equations is given, and a closed-form of Newton method is presented using the compacted Hessian matrix to save the computation and storage required by Newton method. At last, some numerical examples to investigate the convergence and success rate of the proposed method are designed and performed. The performance of the closed-form of Newton method is compared with the Gauss-Newton method as well as the regularization method. The results show that the closed-form of Newton method has good performances even for dealing with ill-posed problems while a great amount of computation is saved. © 2014 Springer-Verlag Berlin Heidelberg.

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