无人机遥感数据处理与滑坡信息提取

doi:10.3724/SP.J.1047.2017.00692

摘要/Abstract

摘要：

高分辨率的DEM和DOM数据是对地形地貌信息的准确描述,也是滑坡信息提取的重要数据源。首先,针对滑坡信息提取的要求,本文采用无人搭载微型单反相机的影像获取平台,结合野外测量的GPS数据,弥补了无人机POS信息精度低的劣势;针对无人机影像的特点,运用摄影测量基本原理与计算机视觉算法,获取高精度、高分辨率的DEM与DOM影像,保留了丰富的光谱与纹理信息。其次,借助ESP辅助工具获取了DOM影像的最佳分割尺度,并结合研究区地物特征构建了基于模糊分类与SVM算法相结合的决策树,运用面向对象的分类方法实现了对研究区内植被、道路、疑似滑坡区域的信息提取。最后,依照研究区地物分布的空间特征确定了高风险等级区域,并对该区域进行滑坡的形态与纹理分析以及精度评价,其中提取的疑似滑坡区域用户精度为91.44%、生产者精度为84.65%,结果表明无人机遥感在滑坡信息提取领域具有较高的应用价值。

关键词: 无人机遥感, 滑坡信息提取, 面向对象分类, 影像处理, DEM, DOM

Abstract:

The high-resolution DEM and DOM data is an accurate description of the topography and geomorphology, and it is also an important source data for landslide information extraction. At first,according to the requirement of landslide information extraction,we use the UAV platform equipped with mini SLR camera combined with the GPS data measured in the field, as the image acquisition method. According to the characteristics of the UAV images, we use the basic principle of photography measurement and computer vision algorithms to obtain the high-resolution DEM and DOM images, which greatly preserves the rich spectral and texture information. Then, with the help of the ESP auxiliary tool we get optimal segmentation scale of the DOM. Based on the fuzzy classification and SVM algorithm to construct a decision tree, which we used to achieve the object oriented classification and information extraction. Finally, according to the spatial feature and distribution of study area we determine the high risk area. By the morphology and texture analysis and accuracy assessment of the landslide area, we show that the producer’s accuracy and user's accuracy of the landslide area are 84.65% , 91.44%. The result proves that the UAV remote sensing has a high value in the field of landslide information extraction.

Key words: UAV remote sensing, landslide information extraction, object-oriented classification, image processing, DEM, DOM

陈天博, 胡卓玮, 魏铼, 胡顺强. 无人机遥感数据处理与滑坡信息提取[J]. 地球信息科学学报, 2017, 19(5): 692-701.

CHEN Tianbo,HU Zhuowei,WEI Lai,HU Shunqiang. Data Processing and Landslide Information Extraction Based on UAV Remote Sensing[J]. Journal of Geo-information Science, 2017, 19(5): 692-701.

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	航线1	航线2a	航线2b
拍摄方式	垂直拍摄	垂直拍摄	倾斜拍摄
航线全长/km	9.4	2.6	3.1
飞行绝对高度/m	950	720	700
航向重叠率/%	80	90	90
旁向重叠率/%	65	70	7
相机芯片尺寸/mm	23.5×15.6	23.5×15.6	23.5×15.6
相片分辨率	6000×4000	6000×4000	6000×4000
飞行区海拔范围/m	389~808	417~650	417~650
最低点地面分辨率/m	0.14	0.07	0.08
照片数/张	238	72	70
相机俯仰角/°	90	90	35

	空中三角测量				光束法平差
	覆盖面积/km²	GSD/cm	每张照片特征点/个	每张照片匹配点/个	平面点/个	平高点/个	反投影误差（像元）
区域1	1.32	8.6	109 411	21 133	8 657 698	3 399 563	0.24
区域2	0.28	5.4	118 108	34 010	2 187 265	879 857	0.17

类别	参考特征	隶属度函数	阈值
植被	VDVI指数		（-0.018, 0.002）
道路	坡度		（14, 16）

	纹理特征
方差	当区域内的灰度变化较大时,该值较高
对比度	表示领域内的灰度差异,影像局部变化越大,值越大
角二阶矩	为同质性的度量,区域内像元值越相近,其同质性越高,该值也越大

地物类型	参考分类结果/m²	实际分类结果/m²	正确分类区域/m²	生产者精度/%	用户精度/%
道路	35 190.39	31 882.42	29 466.97	83.74	92.42
植被	763 596.05	654 317.23	605 215.03	79.26	92.50
疑似滑坡区域	18 331.57	16 971.47	15 518.28	84.65	91.44