Publication: Forest Stress Detection Using Feature Engineering & Selection Approach Optimized for Satellite Imagery

15 December 2025

Accurate detection of forest stress from satellite data depends heavily on selecting informative spectral features. Traditional approaches rely on a limited set of predefined vegetation indices, which may not generalize across environmental conditions.

In this work, SWIFTT partners from the Space Research Institute of Ukraine introduce enhanced maximum informativeness maximum independence (E-MIMI), an efficient and interpretable feature selection strategy that identifies optimal combinations of spectral features from generalized vegetation index classes rather than fixed indices. The method combines a genetic algorithm with a caching mechanism and informativeness-based scoring to reduce computation time while maintaining high accuracy. Applied to Sentinel-2 imagery from two ecologically distinct regions, E-MIMI consistently selected index combinations involving red-edge and shortwave infrared bands—spectral domains known to reflect canopy water content and chlorophyll degradation. E-MIMI demonstrates exceptional computational efficiency, completing feature selection up to 80 times faster and using over 1000 times less memory than other traditional methods on large feature spaces, while also achieving comparable levels of segmentation performance.

By integrating computer vision techniques with biophysically grounded features, the approach supports scalable, ecologically meaningful forest stress monitoring and offers a practical foundation for broader environmental applications requiring interpretable and computationally efficient feature selection.

Read the paper in the link below.