A4 Article in conference proceedings
Minimal learning machine in anomaly detection from hyperspectral images (2020)


Pölönen, Ilkka; Riihiaho, Kimmo; Hakola, Anna-Maria; Annala, Leevi (2020). Minimal learning machine in anomaly detection from hyperspectral images. In Paparoditis, N.; Mallet, C.; Lafarge, F.; 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. et al. (Eds.) XXIV ISPRS Congress, Commission III, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020. Hannover: International Society for Photogrammetry and Remote Sensing, 467-472. DOI: 10.5194/isprs-archives-XLIII-B3-2020-467-2020


JYU authors or editors


Publication details

All authors or editors: Pölönen, Ilkka; Riihiaho, Kimmo; Hakola, Anna-Maria; Annala, Leevi

Parent publication: XXIV ISPRS Congress, Commission III

Parent publication editors: Paparoditis, N.; Mallet, C.; Lafarge, F.; 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.

Place and date of conference: Virtual Event, 31.8.-2.9.2020

Journal or series: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ISSN: 1682-1750

eISSN: 2194-9034

Publication year: 2020

Number in series: XLIII-B3-2020

Pages range: 467-472

Number of pages in the book: 1722

Publisher: International Society for Photogrammetry and Remote Sensing

Place of Publication: Hannover

Publication country: Germany

Publication language: English

DOI: http://doi.org/10.5194/isprs-archives-XLIII-B3-2020-467-2020

Open Access: Publication published in an open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71570


Abstract

Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.


Keywords: remote sensing; spectral imaging; machine learning

Free keywords: minimal learning machine; hyperspectral imaging; anomaly detection; remote sensing


Contributing organizations


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Ministry reporting: Yes

Preliminary JUFO rating: 1


Last updated on 2020-01-09 at 10:47