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Landslides Detection using Aerial Ortho-Images and LiDAR Data

机译:使用空中ortho-Images和LIDAR数据进行山体滑坡检测

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Traditionally the landslide areas were manually measured from aerial stereo-pair by visual interpretation or on-site surveying. Since those two approaches are tedious and cost, researches focus on automatic method for efficient and effective detection for landslides area. For the purpose of automatic landslide detection, its surface and spectral characteristics should be analyzed at first. In the paper, base on a pre-compiled landslide map, we utilize the airborne LiDAR and color aerial ortho-images to analyze the terrain slope, canopy texture, spectral greenness for the landslides. Experimental results, indicate that the greenness is an important index to discriminate the bare soil from vegetation, it is better to use a radiometric calibrated near-infrared image for detecting non-vegetation area automatically. In this preliminary study, the proposed logical intersection method can get a correct detection rate up to 86%, but a large commission error will be introduced. A further modification of the thresholds considering the landslides that are covered by trees, a more reasonable result could be achieved.
机译:传统上,通过视觉解释或现场测量从空中立体对中手动测量滑坡区域。由于这两种方法繁琐且成本,重点研究了对山体滑坡区域的高效和有效检测的自动化方法。出于自动滑坡检测,应首先分析其表面和光谱特性。在论文中,基于预编译的滑坡地图,我们利用了机载激光雷达和彩色空中官能图像来分析地形斜坡,底座纹理,山体滑坡的光谱绿色。实验结果表明,绿色是鉴别植被裸露土壤的重要指标,最好使用用于自动检测非植被面积的辐射校准的近红外图像。在初步研究中,所提出的逻辑交叉点方法可以获得高达86%的正确检测率,但介绍了大型佣金错误。考虑到树木覆盖的山体滑坡的阈值进一步修改,可以实现更合理的结果。

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