Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping
Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping
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DOI：
10.1016/j.rse.2011.05.007
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发表时间：
2011-12-15
影响因子：
13.5
通讯作者：
Bouten, Willem
中科院分区：
文献类型：
--
作者：
Anders, Niels S.;Seijmonsbergen, Arie C.;Bouten, Willem
由于高质量的数字地形数据越来越多，半自动地形测绘技术正在逐步取代传统技术。为了有效地分析如此大量的数据，需要优化自动映射技术的处理。在这种情况下，我们提出了一种新的方法来半自动地图高山地貌分层基于对象的图像分析。我们使用了1米的数字地形模型（DTM）来自激光测高数据从一个山区流域，我们计算出各种陆面参数（I-SP）。LSP的“坡度角”和“地形开阔度”已被组合成一个单一的复合层，用于选择参考材料和描绘训练样本。我们开发了一种新的方法，通过比较训练样本和图像对象的二维频率分布矩阵来半自动评估分割结果。分割精度评估使我们能够自动优化用于分割的尺度参数和LSP。我们的结论是，不同的地貌特征类型有不同的最佳分割参数集。将特征相关参数用于分层特征提取的新方法中，对喀斯特、冰川、河流和剥蚀地貌进行分类。在这种方式中，我们已经使用分层的基于对象的图像分析，半自动提取对比地貌特征，从高分辨率的数字地形数据。另一个步骤是使分类规则的优化自动化。然后，我们将能够创建一个特征库，可以将其转移并应用于其他山区，并进一步自动化地貌制图策略。(C)2011 Elsevier Inc. All rights reserved.
Semi-automated geomorphological mapping techniques are gradually replacing classical techniques due to increasing availability of high-quality digital topographic data. In order to efficiently analyze such large amounts of data, there is a need for optimizing the processing of automated mapping techniques. In this context, we present a novel approach to semi-automatically map alpine geomorphology using stratified object-based image analysis. We used a 1 m Digital Terrain Model (DTM) derived from laser altimetry data from a mountainous catchment from which we calculated various Land-Surface Parameters (I-SPs). The LSPs 'slope angle' and 'topographic openness' have been combined into a single composite layer for selecting reference material and delineating training samples. We developed a novel method to semi-automatically assess segmentation results by comparing 2D frequency distribution matrices of training samples and image objects. The segmentation accuracy assessment allowed us to automate optimization of the scale parameter and LSPs used for segmentation. We concluded that different geomorphological feature types have different sets of optimal segmentation parameters. The feature-dependent parameters were used in a new approach of stratified feature extraction for classifying karst, glacial, fluvial and denudational landforms. In this way, we have used stratified object-based image analysis to semi-automatically extract contrasting geomorphological features from high-resolution digital terrain data. A further step would be to also automate the optimization of classification rules. We would then be able to create a library of feature characteristics that could be transferred and applied to other mountain regions and further automate geomorphological mapping strategies. (C) 2011 Elsevier Inc. All rights reserved.