Obia-based extraction of artificial terrace damages in the loess plateau of china from uav photogrammetry
Obia-based extraction of artificial terrace damages in the loess plateau of china from uav photogrammetry
复制标题
基于Obia的无人机摄影测量中国黄土高原人工梯田病害提取
DOI：
10.3390/ijgi10120805
复制
发表时间：
2021
影响因子：
3.4
通讯作者：
Ding H.
中科院分区：
文献类型：
--
作者：
Fang X.;Li J.;Zhu Y.;Cao J.;Na J.;Jiang S.;Ding H.
梯田是世界范围内典型的人工地貌，对农业生产和水土保持具有重要意义。然而，由于缺乏维护，梯田经常发生破坏，影响局部水流过程，进而影响土壤侵蚀。台地灾害自动高精度制图是台地灾害监测和相关研究的基础。目前，人工梯田灾害作图主要是通过人工实地调查实现的，但缺乏自动作图的方法。鉴于高分辨率无人机（UAV）摄影测量和基于目标的图像分析（OBIA）在图像处理任务中的成功，本研究提出了一种基于OBIA和无人机摄影测量的平台损伤制图集成框架。以黄土高原普家洼阶地为研究区。首先，结合高分辨率图像和数字表面模型获取的光谱特征和地形及相应纹理，对分割过程进行优化；通过相关性分析实现特征选择，利用尺度参数估计算法获得最优分割参数。然后，使用监督k近邻分类器识别分割对象中的阶地损伤，并考虑对象层面的附加几何特征进行分类。通过图像和实地调查与地面真实情况的比较，表明所提出的分类可以充分地执行。3种阶地破坏的提取f值分别为92.07%（阶地塌陷）、81.95%（脊状塌陷）和85.17%（崩塌），Kappa系数为85.34%。最后，确定了阶地损伤在本研究中的潜在应用和空间分布。本研究可为黄土高原阶地破坏制图提供一个可靠的框架。
Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China.