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

Salii, Y. V.; Lavreniuk, A. M.; Kussul, N. M.

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

Файли

86-98.pdf (965.94 KB)

Дата

2024

Автори

Salii, Y. V.

Lavreniuk, A. M.

Kussul, N. M.

Видавець

КПІ ім. Ігоря Сікорського

Анотація

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.

Ключові слова

Sentinel-2, vegetation indices, Bhattacharyya distance, feature engineering, greedy algorithms, Spearman’s rank correlation coefficient, вегетаційні індекси, відстань Бгаттачар’я, інженерія ознак, жадібні алгоритми, коефіцієнт кореляції Спірмена

Бібліографічний опис

Statistical methods of feature engineering for the problem of forest state classification using satellite data / Salii Y. V., Lavreniuk A. M., Kussul N. M. // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2024. – № 1. – С. 86-98. – Бібліогр.: 11 назв.