A4 Article in conference proceedings
Updating strategies for distance based classification model with recursive least squares (2022)
Raita-Hakola, A.-M., & Pölönen, I. (2022). Updating strategies for distance based classification model with recursive least squares. In J. Jiang, A. Shaker, & H. Zhang (Eds.), XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III (V-3-2022, pp. 163-170). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprs-annals-V-3-2022-163-2022
JYU authors or editors
Publication details
All authors or editors: Raita-Hakola, Anna-Maria; Pölönen, Ilkka
Parent publication: XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III
Parent publication editors: Jiang, J.; Shaker, A.; Zhang, H.
Conference:
- International Society for Photogrammetry and Remote Sensing Congress
Place and date of conference: Nice, France, 6.-11.6.2022
Journal or series: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISSN: 2194-9042
eISSN: 2194-9050
Publication year: 2022
Publication date: 17/05/2022
Volume: V-3-2022
Pages range: 163-170
Number of pages in the book: 711
Publisher: Copernicus Publications
Publication country: Germany
Publication language: English
DOI: https://doi.org/10.5194/isprs-annals-V-3-2022-163-2022
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/82587
Abstract
The results show that the new self-learning MLM method can classify new classes with RLS update but with a cost of decreasing accuracy. With a larger amount of reference points, one class can be introduced with reasonable accuracy. The results of experiment B indicate that self-learning MLM can be trained with a few reference points, and the self-learning model quickly reaches accuracy results comparable with nearest-neighbour NN-MLM. It seems that the self-learning MLM could be a comparable machine learning method for the application of hyperspectral imaging and remote sensing.
Keywords: hyperspectral imaging; remote sensing; classification; machine learning
Free keywords: hyperspectral imaging; Minimal Learning Machine; Recursive Least Squares; classification; real-time computation; machine learning
Contributing organizations
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- Pölönen, Ilkka
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Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 1