论文标题
使用基于深度和张量的高光谱图像学习文化古迹的自动检查
Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery
论文作者
论文摘要
在文化遗产中，高光谱图像通常使用，因为它们提供了有关材料光学特性的扩展信息。因此，从要应用的机器学习技术的角度来看，这种高维数据的处理变得具有挑战性。在本文中，我们提出了一种基于排名的基于tensor的学习模型，以识别和对文化遗产纪念碑的物质缺陷进行分类。与常规的深度学习方法相反，拟议的高阶基于张量的学习表明，具有更高的准确性和鲁棒性，可抵抗过度拟合。来自联合国教科文组织保护区的现实世界数据的实验结果表明，与常规深度学习模型相比，该计划的优越性。
In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-$R$ tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimental results on real-world data from UNESCO protected areas indicate the superiority of the proposed scheme compared to conventional deep learning models.
