Статистичні методи інженерії ознак для задачі класифікації стану лісів за супутниковими даними

• Main Authors: Salii, Yevhenii, Lavreniuk, Alla, Kussul, Nataliia
• Format: Article
• Language: English
• Published: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2024
• Subjects: Sentinel-2; вегетаційні індекси; відстань Бгаттачар’я; інженерія ознак; жадібні алгоритми; коефіцієнт кореляції Спірмена; vegetation indices; Bhattacharyya distance; feature engineering; greedy algorithms; Spearman’s rank correlation coefficient
• Online Access: http://journal.iasa.kpi.ua/article/view/286178

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.