Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient Tree Cover Estimation
This is an incremental improvement for urban forestry monitoring using satellite imagery.
The paper tackles tree cover estimation in urban areas by proposing a multi-spectral random forest classifier with feature selection and masking, which outperforms conventional random forest, ESA WorldCover 10m 2020, and DeepLabv3 on an 82-acre area of LUMS.
This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using spectral indices followed by random forest classification on the remaining mask with carefully selected features. Using Sentinel-2 satellite imagery, we evaluate the performance of the proposed technique on a specified area (approximately 82 acres) of Lahore University of Management Sciences (LUMS) and demonstrate that our method outperforms a conventional random forest classifier as well as state-of-the-art methods such as European Space Agency (ESA) WorldCover 10m 2020 product as well as a DeepLabv3 deep learning architecture.