A4 Article in conference proceedings
Piecewise anomaly detection using minimal learning machine for hyperspectral images (2021)

Raita-Hakola, A.-M., & Pölönen, I. (2021). Piecewise anomaly detection using minimal learning machine for hyperspectral images. In N. Paparoditis, C. Mallet, F. Lafarge, M. Y. Yang, J. Jiang, A. Shaker, H. Zhang, X. Liang, B. Osmanoglu, U. Soergel, E. Honkavaara, M. Scaioni, J. Zhang, A. Peled, L. Wu, R. Li, M. Yoshimura, K. Di, O. Altan, H. M. Abdulmuttalib, & F. S. Faruque (Eds.), XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission III (pp. 89-96). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2021. https://doi.org/10.5194/isprs-annals-V-3-2021-89-2021

JYU authors or editors

Publication details

All authors or editors: Raita-Hakola, A.-M.; Pölönen, I.

Parent publication: XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission III

Parent publication editors: Paparoditis, N.; Mallet, C.; Lafarge, F.; Yang, M. Y.; Jiang, J.; Shaker, A.; Zhang, H.; Liang, X.; Osmanoglu, B.; Soergel, U.; Honkavaara, E.; Scaioni, M.; Zhang, J.; Peled, A.; Wu, L.; Li, R.; Yoshimura, M.; Di, K.; Altan, O.; Abdulmuttalib, H. M.; Faruque, F. S.

Conference:

Place and date of conference: Digital Event, 5.-9.7.2021

Journal or series: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ISSN: 2194-9042

eISSN: 2194-9050

Publication year: 2021

Publication date: 17/06/2021

Number in series: V-3-2021

Pages range: 89-96

Number of pages in the book: 324

Publisher: Copernicus Publications

Publication country: Germany

Publication language: English

DOI: https://doi.org/10.5194/isprs-annals-V-3-2021-89-2021

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/77765

Abstract

Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.

Keywords: spectral imaging; hyperspectral imaging; machine learning

Free keywords: hyperspectral imaging; Minimal Learning Machine; piecewise approach; anomaly detection; real-time computation; machine learning

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2021

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