Statistical methods of feature engineering for the problem of forest state classification using satellite data

Authors

  • Yevhenii Salii Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0009-0006-0395-8099
  • Alla Lavreniuk Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0000-0002-5791-0377
  • Nataliia Kussul Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0000-0002-9704-9702

DOI:

https://doi.org/10.20535/SRIT.2308-8893.2024.1.07

Keywords:

Sentinel-2, vegetation indices, Bhattacharyya distance, feature engineering, greedy algorithms, Spearman’s rank correlation coefficient

Abstract

Timely detection of forest diseases is an important task for their prevention and spread limitation. The usage of satellite imagery provides capabilities for large-scale forest monitoring. Machine learning models allow to automate the analysis of these data for anomaly detection indicating diseases. However, selecting informative features is key to building an effective model. In this work, the application of Bhattacharyya distance and Spearman’s rank correlation coefficient for feature selection from satellite images was investigated. A greedy algorithm was applied to form a subset of weakly correlated features. The experiment showed that selected features allow for improving the classification quality compared to using all spectral bands. The proposed approach demonstrates effectiveness for informative and weakly correlated feature selection and can be utilized in other remote sensing tasks.

Author Biographies

Yevhenii Salii, Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Student at the Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine.

Alla Lavreniuk, Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Candidate of Technical Sciences (Ph.D.), an associate professor at the Department of Mathematical Modelling and Data Analysis of the Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine.

Nataliia Kussul, Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Professor, Doctor of Technical Sciences, the head of the Department of Mathematical Modelling and Data Analysis of the Educational and Research Institute of Physics and Technology of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine.

References

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Published

2024-03-29

Issue

Section

Theoretical and applied problems of intelligent systems for decision making support