Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery

• URL: http://arxiv.org/abs/2207.02163v1
• Date: Tue, 5 Jul 2022 16:38:27 GMT
• Title: Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery
• Authors: Ioannis N. Tzortzis, Ioannis Rallis, Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos
• Abstract summary: We propose a Rank-$R$ tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. Experimental results on real-world data from UNESCO protected areas indicate the superiority of the proposed scheme.
• Score: 12.150176109067218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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