State Bureau of Surveying and Mapping

About: State Bureau of Surveying and Mapping is a based out in . It is known for research contribution in the topics: Terrain & Pixel. The organization has 277 authors who have published 206 publications receiving 2044 citations.

Block adjustment of POS-supported airborne SAR images

Abstract: The regional network adjustment of airborne SAR imagery is the important matter of realizing topographic survey and mapping of airborne SAR imagery with few control points. This paper, firstly, analyzes geometric relationship of airborne POS intergration SAR sensor and error characteristic of GPS data of SAR strip imagers, to construct the model of airborne SAR imagery space triangulation survey with auxiliary by POS, based on R-D imagery construction function. Positioning tests are been carried out respectively with the different number and distribution of control point, using the images of the mountain areas in western China, collected by multi-band and multi-polarization airborne SAR mapping system, as data source, and there is a close relationship of distribution of control point, three-dimensional photography baseline length and so on. Under the condition of less control points, higher accuracy can be achieved of the airborne SAR imagery, which indicates that the model this paper presents is right and feasible. In addition, the model constructed and the high resolution airborne SAR images used in this paper can be applied in middle and big scale topographic survey and mapping.

Digital terrain model extraction from airborne LiDAR data in complex mining area

Abstract: Airborne light detection and ranging (LiDAR) proved to be an adequate technique to deliver highly accurate 3D mass points of the surface. However, the surface of mining area is complex with steep slope, dense vegetation, artificial mining facilities and buildings, which is different from the flat surface of city. The main processing workflow for DTM generation from LiDAR includes points filtering and DEM interpolation. In this article, five methods for removing object points from LiDAR data in mining area were compared. These methods, including Adaptive TIN (ATIN), Elevation Threshold with Expand Window (ETEW), Maximum Local Slope (MLS), Mathematical morphology (MM), Iterative Polynomial Fitting (IPF), analyze data points based on variations of local slope, elevation and height difference between points and the interpolated surfaces. Complex mining area data set with various cliffs of quarry, trees, houses, roads and small reliefs were selected to test the filtering methods. The results show that all methods can effectively remove most object points in complex mining areas. The ATIN and MM filter generated the best result in sharp cliff area of a quarry, whereas the other algorithms tended to remove the steep edge of quarry and roads. Depending on the filtering parameters, each method experienced various omission or commission errors. Quantitative assessment shows the ATIN and IPF based on the height difference between points and surface perform better. DEM interpolation assessment experiments indicate that interpolation biases were minute. Global statistics show that Modified shepard's method, Spline and Radial basis function interpolation methods have the lowest errors in the study area.

Sea ice concentration over the Antarctic Ocean from satellite pulse altimetry

Abstract: Sea ice concentration (SIC) is an important parameter in characterizing sea ice. Limited by the environment and the spatial extent of observation, it is difficult for field work to meet the needs of a large-scale SIC study. However, with its many advantages, such as the ability to make large-scale, high-resolution and long-duration observations, the altimeter can be used to determine SIC on a large scale. Using the correspondence between the satellite pulse altimeter waveform and reflector property, waveform classification is employed. Moreover, this paper develops an algorithm to obtain the SIC from altimeter waveforms. In an actual computation, Pyrz Bay in the Antarctic is taken as an experimental region, and one-year and seasonal SICs are derived from ERS-1/GM waveforms over this study area. Furthermore, altimetric SICs are compared with those of SSMR SSM/I. The results show that the spatial distribution and the regions of maximum SIC determined employing these two methods are consistent. This demonstrates that altimeter data can be used to monitor sea ice.

Precise orbit determination and accuracy analysis of HY-2A satellite using DORIS Doppler data

Abstract: HY-2A is the first ocean dynamic environment monitoring satellite of China, and its precise radial orbit is a critical factor to its scientific research application, for example, its orbit can serve as a reference frame for altimeter measurement. In order to realize precise orbit determination, HY-2A is equipped with Doppler orbitography and radiopositioning integrated by satellite (DORIS), retro-reflector array for the satellite laser ranging (SLR) and global positioning system tracking systems. In this study, DORIS Doppler data is used for its good reputation for precise orbit positioning. We described the strategy of orbit determination using dynamic models in detail, and particularly analyzed the relationship between arc length, orbital determination accuracy, and consumed time, and then concluded that 3-day is the optimal arc length. We carried out detail accuracy assessments of external orbit comparison, overlap orbit validation and SLR range validation. Experimental results show that the radial accuracy can reach 0.0154 m for the 3-day arc, and the radial overlap validation accuracy is better than 0.0223 m, and the accuracy of SLR range validation is better than 0.0964 m. Results demonstrate that the DORIS system meets the orbit precision specifications of HY-2A satellite. Difference between computed orbit and the SSALTO orbit in the radial, along track, and cross track directions from 00:00:25 on September 8 to 00:00:25 on September11, 2012 using DORIS Doppler data.

Neural Network Classification with Optimization by Genetic Algorithms for Remote Sensing Imagery

Abstract: This paper presents a new method based on genetic algorithms(GA) optimized back propagation neural network classification for remote sensing imagery,which overcomes the defects that traditional approaches based on statistical principle have difficulties in distinguishing the objects with similar spectral characteristics,and the back propagation(BP) neural network method has difficulties in sufficiency and convergence.On the basis of the typical back propagation neural network classification,the optimization for the structure of neural network by genetic algorithms is presented,including the thresholds and connection weights of neural network and the number of neural nodes in the hidden layer of neural network.A coding method on float coding with variable length for genetic algorithms is then introduced,and the evolution method is improved to obtain an optimal back propagation neural network.In the end,an experimental test on remote sensing classification by using Landsat thematic mapper(TM) imagery in Dianshan Lake is conducted.The classification with the proposed approach is the most accurate,which proves its feasibility and validity.