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 editorsRaita-Hakola, Anna-Maria; Pölönen, Ilkka

Parent publicationXXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III

Parent publication editorsJiang, J.; Shaker, A.; Zhang, H.

Conference:

  • International Society for Photogrammetry and Remote Sensing Congress

Place and date of conferenceNice, France6.-11.6.2022

Journal or seriesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ISSN2194-9042

eISSN2194-9050

Publication year2022

Publication date17/05/2022

VolumeV-3-2022

Pages range163-170

Number of pages in the book711

PublisherCopernicus Publications

Publication countryGermany

Publication languageEnglish

DOIhttps://doi.org/10.5194/isprs-annals-V-3-2022-163-2022

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/82587


Abstract

The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment B is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment B aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach maximum classification accuracy quickly.

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.


Keywordshyperspectral imagingremote sensingclassificationmachine learning

Free keywordshyperspectral imaging; Minimal Learning Machine; Recursive Least Squares; classification; real-time computation; machine learning


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Related projects


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-26-03 at 09:21