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https://www.um.edu.mt/library/oar/handle/123456789/125533
Title: | Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery |
Authors: | Tzortzis, Ioannis N. Rallis, Ioannis Makantasis, Konstantinos Doulamis, Anastasios Doulamis, Nikolaos Voulodimos, Athanasios |
Keywords: | Hyperspectral imaging -- Classification Tensor products Cultural property -- Data processing -- Case studies Imaging systems -- Remote sensing |
Issue Date: | 2022-10 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | Tzortzis, I. N., Rallis, I., Makantasis, K., Doulamis, A., Doulamis, N., & Voulodimos, A. (2022, October). Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery. IEEE International Conference on Image Processing ICIP2022, Bordeaux. 3136-3140. |
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. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/125533 |
Appears in Collections: | Scholarly Works - FacICTAI |
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File | Description | Size | Format | |
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Automatic inspection of cultural monuments using deep and tensor based learning on hyperspectral imagery 2022.pdf Restricted Access | 4.31 MB | Adobe PDF | View/Open Request a copy |
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