ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 163–170, 2022
https://doi.org/10.5194/isprs-annals-V-3-2022-163-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 163–170, 2022
https://doi.org/10.5194/isprs-annals-V-3-2022-163-2022
 
17 May 2022
17 May 2022

UPDATING STRATEGIES FOR DISTANCE BASED CLASSIFICATION MODEL WITH RECURSIVE LEAST SQUARES

A.-M. Raita-Hakola and I. Pölönen A.-M. Raita-Hakola and I. Pölönen
  • Faculty of Information Technology, University of Jyväskylä, 40100, Jyväskylä, Finland

Keywords: Hyperspectral imaging, Minimal Learning Machine, Recursive Least Squares, Classification, Real-time computation, Machine learning

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.