05 September 2024

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

In this work, SWIFTT partners from the Space Research Institute of Ukraine and collaborators discuss 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 them to obtain highly informative set of features with minimized redundancy within huge feature space with moderate amount of computation.

Read the paper in the link below.