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

arXiv: https://arxiv.org/abs/2207.02163 · HTML: https://mlsanity.com/paper/2207.02163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Tensor-Based Classifiers for Hyperspectral Data Analysis](https://mlsanity.com/paper/1709.08164/) (2017)
- [Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification](https://mlsanity.com/paper/2104.05048/) (2021)
- [Common Mode Patterns for Supervised Tensor Subspace Learning](https://mlsanity.com/paper/1902.02075/) (2019)

## Abstract (arXiv)

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.