University of Oulu

Uteng, S.; Johansen, T.H.; Zaballos, J.I.; Ortega, S.; Holmström, L.; Callico, G.M.; Fabelo, H.; Godtliebsen, F. Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images. Appl. Sci. 2020, 10, 2298. https://doi.org/10.3390/app10072298

Early detection of change by applying scale-space methodology to hyperspectral images

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Author: Uteng, Stig1; Haugland Johansen, Thomas2; Ignacio Zaballos, Jose3;
Organizations: 1Department of Education and Pedagogy, UiT The Arctic University of Norway, 9019 Tromsø, Norway
2Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9019 Tromsø, Norway
3Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain
4Research Unit of Mathematical Sciences, University of Oulu, 90570 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020041516730
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2020-04-15
Description:

Abstract

Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples include identification of potentially malignant changes in skin moles or the gradual onset of food quality deterioration. Statistical scale-space methodologies can be very useful in such situations since exploring the measurements in multiple resolutions can help identify even subtle changes. We extend a recently proposed scale-space methodology to a technique that successfully detects such small changes and at the same time keeps false alarms at a very low level. The potential of the novel methodology is first demonstrated with hyperspectral skin mole data artificially distorted to include a very small change. Our real data application considers hyperspectral images used for food quality detection. In these experiments the performance of the proposed method is either superior or on par with a standard approach such as principal component analysis.

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Series: Applied sciences
ISSN: 2076-3417
ISSN-E: 2076-3417
ISSN-L: 2076-3417
Volume: 10
Issue: 7
Article number: 2298
DOI: 10.3390/app10072298
OADOI: https://oadoi.org/10.3390/app10072298
Type of Publication: A1 Journal article – refereed
Field of Science: 112 Statistics and probability
217 Medical engineering
222 Other engineering and technologies
Subjects:
Funding: This project is supported by Tromsø Research Foundation through TFS project ID: 16_TF_FG.
Copyright information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/