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首页> 外文会议>ISPRS Congress >LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION
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LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION

机译：使用机载激光扫描数据衍生地形地形属性和支持向量机分类

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Since the availability of high-resolution Airborne Laser Scanning (ALS) data, substantial progress in geomorphological research, especially in landslide analysis, has been carried out. First and second order derivatives of Digital Terrain Model (DTM) have become a popular and powerful tool in landslide inventory mapping. Nevertheless, an automatic landslide mapping based on sophisticated classifiers including Support Vector Machine (SVM), Artificial Neural Network or Random Forests is often computationally time consuming. The objective of this research is to deeply explore topographic information provided by ALS data and overcome computational time limitation. For this reason, an extended set of topographic features and the Principal Component Analysis (PCA) were used to reduce redundant information. The proposed novel approach was tested on a susceptible area affected by more than 50 landslides located on Roznow Lake in Carpathian Mountains, Poland. The initial seven PCA components with 90% of the total variability in the original topographic attributes were used for SVM classification. Comparing results with landslide inventory map, the average user's accuracy (UA), producer's accuracy (PA), and overall accuracy (OA) were calculated for two models according to the classification results. Thereby, for the PCA-feature-reduced model UA, PA, and OA were found to be 72%, 76%, and 72%, respectively. Similarly, UA, PA, and OA in the non-reduced original topographic model, was 74%, 77% and 74%, respectively. Using the initial seven PCA components instead of the twenty original topographic attributes does not significantly change identification accuracy but reduce computational time.
机译：由于高分辨率空气激光扫描（ALS）数据的可用性，已经进行了地貌研究的实质性进展，特别是在滑坡分析中。数字地形模型（DTM）的第一和二阶衍生物已成为Landslide库存映射中的流行和强大的工具。然而，基于包括支持向量机（SVM）的复杂分类器的自动滑坡映射，人工神经网络或随机林通常是计算耗时的。本研究的目的是深入探索ALS数据提供的地形信息并克服计算时间限制。因此，使用扩展的正版功能和主成分分析（PCA）来减少冗余信息。在波兰喀尔巴阡山脉的Roznow湖上受到超过50个滑坡影响的易感区域测试了拟议的新方法。最初的七个PCA组件，具有90％的原始地形属性的总变异性用于SVM分类。根据分类结果，将平均用户的准确性（UA），生产者的精度（PA）和整体精度（OA）进行比较。由此，对于PCA特征减少的模型UA，PA和OA分别被发现为72％，76％和72％。类似地，UA，PA和OA在非降低的原始地形模型中，分别为74％，77％和74％。使用初始七个PCA组件而不是20个原始地形属性不会显着改变识别准确性，而是降低计算时间。

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