论文标题
使用无监督和半监督系统的自动陨石坑的检索
Automatic Crater Shape Retrieval using Unsupervised and Semi-Supervised Systems
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Impact craters are formed due to continuous impacts on the surface of planetary bodies. Most recent deep learning-based crater detection methods treat craters as circular shapes, and less attention is paid to extracting the exact shapes of craters. Extracting precise shapes of the craters can be helpful for many advanced analyses, such as crater formation. This paper proposes a combination of unsupervised non-deep learning and semi-supervised deep learning approach to accurately extract shapes of the craters and detect missing craters from the existing catalog. In unsupervised non-deep learning, we have proposed an adaptive rim extraction algorithm to extract craters' shapes. In this adaptive rim extraction algorithm, we utilized the elevation profiles of DEMs and applied morphological operation on DEM-derived slopes to extract craters' shapes. The extracted shapes of the craters are used in semi-supervised deep learning to get the locations, size, and refined shapes. Further, the extracted shapes of the craters are utilized to improve the estimate of the craters' diameter, depth, and other morphological factors. The craters' shape, estimated diameter, and depth with other morphological factors will be publicly available.
