Topography-driven satellite imagery analysis for landslide mapping
Topography-driven satellite imagery analysis for landslide mapping
复制标题
DOI：
10.1080/19475705.2018.1458050
复制
发表时间：
2018-01-01
影响因子：
4.2
通讯作者：
Marchesini, I.
中科院分区：
文献类型：
--
作者：
Alvioli, M.;Mondini, A. C.;Marchesini, I.
我们描述了一种半自动程序，用于将卫星图像分类为滑坡或无滑坡类别，旨在准备事件滑坡清单地图。两步程序需要了解滑坡事件的发生、事件前后伪立体对的可用性以及数字高程模型。第一步包括评估判别函数，该函数应用于根据滑坡光谱响应调整的众所周知的变化检测指数的组合。第二步致力于判别函数分类，旨在通过改进通常的阈值方法来区分唯一的滑坡类别。我们设计了一种多阈值分类，其中在场景的小子集中单独应用阈值。我们表明，与通过视觉解释准备的滑坡清单的地面实况相比，使用坡度单位作为地形感知子集可以产生最佳的分类性能。事实证明，该方法优于使用单个阈值以及任何基于场景地形盲细分的多阈值程序，尤其是在验证阶段。我们认为，改进的分类性能和有限的训练要求代表着向利用卫星图像自动实时绘制滑坡测绘迈出了一步。
We describe a semi-automatic procedure for the classification of satellite imagery into landslide or no landslide categories, aimed at preparing event landslide inventory maps. The two-steps procedure requires knowledge of the occurrence of a landslide event, availability of a pre- and post- event pseudo-stereo pair and a digital elevation model. The first step consists in the evaluation of a discriminant function, applied to a combination of well-known change detection indices tuned on landslide spectral response. The second step is devoted to discriminant function classification, aimed at distinguishing the only landslide class, through an improvement of the usual thresholding' method. We devised a multi-threshold classification, in which thresholding is applied separately in small subsets of the scene. We show that using slope units as topographic-aware subsets produces best classification performance when compared to the ground truth of a landslide inventory prepared by visual interpretation. The method proved to be superior to the use of a single threshold and to any multi-threshold procedure based on topography-blind subdivisions of the scene, especially in the validation stage. We argue that the improved classification performance and limited training requirements represent a step forward towards an automatic, real-time landslide mapping from satellite imagery.