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

TL;DR

This paper introduces a tensor-based machine learning model for inspecting cultural monuments using hyperspectral imagery, achieving higher accuracy and robustness than traditional deep learning methods.

Contribution

It presents a novel Rank-R tensor-based learning approach for material defect classification in cultural heritage, outperforming conventional deep learning models.

Findings

Tensor-based model shows higher accuracy.

Model is more robust against overfitting.

Outperforms traditional deep learning methods.

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.