Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient
  Tree Cover Estimation
Usman Nazir Momin Uppal Muhammad Tahir Zubair Khalid
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.