The traditional surveying and mapping of ancient landslide is mainly based on GPS, which has high precision but low efficiency. As a new remote sensing technology, synthetic aperture radar interferometry (InSAR) has developed into one of the effective means to quickly obtain high-precision digital elevation model. In this paper, the shenjiazui ancient landslide formed by the Haiyuan earthquake in 1920 is taken as the research object. The elevation information of the ancient landslide is obtained by three means: field RTK measurement, DEM extraction based on Sentinel-1A radar data and SRTM download. The DEM accuracy is compared by three methods: checkpoint method, profile method and contour playback method. Through the precision comparison and analysis, it is found that the DEM extracted by Sentinel-1A is in good agreement with the DEM measured by field RTK. The mean error is within 0.6m and the mean square error is within 9m, which is much better than the currently disclosed SRTM precision. It can quickly, widely and accurately obtain the surface 3D data and apply it to the 3D modeling of ancient landslide.
In this paper a novel fully automated 3D reconstruction approach based on low-altitude unmanned aerial vehicle system (UAVs) images will be presented, which does not require previous camera calibration or any other external prior knowledge. Dense 3D point clouds are generated by integrating orderly feature extraction, image matching, structure from motion (SfM) and multi-view stereo (MVS) algorithms, overcoming many of the cost, time limitations of rigorous photogrammetry techniques. An image topology analysis strategy is introduced to speed up large scene reconstruction by taking advantage of the flight-control data acquired by UAV. Image topology map can significantly reduce the running time of feature matching by limiting the combination of images. A high-resolution digital surface model of the study area is produced base on UAV point clouds by constructing the triangular irregular network. Experimental results show that the proposed approach is robust and feasible for automatic 3D reconstruction of low-altitude UAV images, and has great potential for the acquisition of spatial information at large scales mapping, especially suitable for rapid response and precise modelling in disaster emergency.
KEYWORDS: Remote sensing, Image processing, Data acquisition, Image compression, Digital image processing, Visualization, Environmental sensing, C++, Image storage, Microsoft Foundation Class Library
The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.
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