基于深度学习和太阳系天体初步结果的陆地水系网络分类新方法
A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies
摘要：
提出了几种方法来描述排水网络的地貌和非生物/生物因素决定其形态。有一个内在的复杂性的明确资格的形态变化，以响应各种类型的控制因素和困难的表达因果关系。传统的排水管网分类方法是基于人工提取关键特征，然后作为模式识别方案。然而，这些方法具有较低的预测能力和一致性。我们提出了一种不同的方法，基于数据驱动的图像监督学习，也扩展到外星人的情况下。通过深度学习模型，提取和分类阶段被集成在一个更客观、分析和自动化的框架中。尽管最初的困难，由于可用的训练图像数量很少，以及排水样本的不同形状之间的相似性，我们获得了成功的结果，得出的结论是，深度学习是地貌学和相关领域数据探索的有效方法。
英文摘要：
Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.
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