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首页> 外文期刊>Earth Surface Processes and Landforms: The journal of the British Geomorphological Research Group >Advances in automated detection of sand dunes on Mars
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Advances in automated detection of sand dunes on Mars

机译：在火星上自动检测沙丘的进展

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This paper describes advances in an automatic approach for the detection of sand dunes of Mars, based on supervised learning techniques. A set of features (gradient histogram) is extracted from the remotely sensed images and two classifiers (Support Vector Machine and Random Forests) are trained from this data. The evaluation is conducted on 230 MOC-NA images (spatial resolution between 1·45 and 6·80 m/pixel) leading to about 89% of correct detections. A detailed analysis of the detection results (duneon-dune) is performed by dune type or bulk shape, confirming high performances independently of the way the dataset is analysed. This demonstrates the robustness and adequacy of the automated approach to deal with the large variety of aeolian structures present on the surface of Mars.
机译：本文介绍了基于监督学习技术的火星沙丘自动检测方法的进展。从遥感图像中提取出一组特征（梯度直方图），并从该数据中训练出两个分类器（支持向量机和随机森林）。对230个MOC-NA图像（空间分辨率介于1·45和6·80 m /像素之间）进行评估，可获得大约89％的正确检测结果。对检测结果（沙丘/非沙丘）的详细分析通过沙丘类型或大块形状进行，独立于数据集的分析方式确认了高性能。这证明了自动方法处理火星表面上各种风沙结构的鲁棒性和充分性。

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