Features’ Selection for Forest State Classification Using Machine Learning on Satellite Data

Salii, Yevhenii; Kuzin, Volodymyr; Lavreniuk, Alla; Kussul, Nataliia; Shelestov, Andrii

Features’ Selection for Forest State Classification Using Machine Learning on Satellite Data

Файли

Features_Selection.pdf (1.8 MB)

Дата

2024

Автори

Видавець

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Анотація

This paper discusses the use of advanced computer vision and artificial intelligence techniques for analysing remote sensing data, specifically focusing on the semantic segmentation of forest areas. The goal is to identify forest damage caused by insect pests using multispectral images from Sentinel-2 satellites. The proposed approach involves using genetic algorithms to automatically select informative features based on vegetation indices. A new fitness function is introduced to assess the quality of the selected feature sets. The neural network is then trained and tested using real data. The results of the study show the effectiveness of proposed approach and highlight its advantages over traditional methods. The developed technique allowed to obtain highly informative set of features with minimized redundancy within huge feature space with moderate amount of computation.

Ключові слова

Bhattacharyya distance, remote sensing, feature engineering, vegetation indices, Sentinel-2

Бібліографічний опис

Features’ Selection for Forest State Classification Using Machine Learning on Satellite Data / Yevhenii Salii, Volodymyr Kuzin, Alla Lavreniuk, Nataliia Kussul, Andrii Shelestov // IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 7-12 July, 2024, Athens, Greece. - Athens, 2024. - P. 9874-9878.