Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data
Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data
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DOI：
10.1029/2023ea002845
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发表时间：
2023-05
影响因子：
3.1
通讯作者：
Aaron E. Maxwell;W. Odom;C. Shobe;D. Doctor;Michelle S. Bester;Tobi Ore
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作者：
Aaron E. Maxwell;W. Odom;C. Shobe;D. Doctor;Michelle S. Bester;Tobi Ore
地球表面过程和景观演变的许多研究依赖于精确和广泛的地表地质单元和地貌数据集。使用深度学习自动提取地貌特征提供了一种客观的方法，可以在大空间范围内一致地绘制地貌。然而，对于这种分析的最佳输入特征空间没有共识。我们探索了输入特征空间对使用基于卷积神经网络（CNN）的语义分割深度学习从数字地形模型（DTM）导出的地表参数（LSP）中提取地貌特征的影响。我们比较了四种输入特征空间配置：（a）由使用50 m半径圆形窗口计算的地形位置指数（TPI）组成的三层复合物，地形坡度的平方根，以及使用2 m内径和10 m外径的环形计算的TPI，（B）单个照明位置山体阴影，（c）多方向山体阴影，以及（d）斜坡阴影。我们使用三种深度学习算法和四种用例测试每个特征空间输入：两种使用自然特征，两种使用人为特征。三层复合材料通常为训练样本提供更低的总体损失，为保留的验证数据提供更高的F1分数，并且从新的地理范围推广到保留的测试数据的性能更好。结果表明，基于CNN的深度学习从LSP映射地貌特征或地形对输入特征空间敏感。考虑到可以从DTM数据中导出的大量LSP以及可以使用基于CNN的方法进行的各种地貌映射任务，我们认为需要进行更多的研究，重点关注特征空间的考虑，并建议未来的研究方向。我们还建议，与使用山体阴影或其他常见地形可视化表面相比，这里实现的三层复合可以提供更好的性能，因此值得考虑用于不同的映射和特征提取任务。
Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)‐based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three‐layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three‐layer composite generally provided lower overall losses for the training samples, a higher F1‐score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN‐based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN‐based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three‐layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.