1. Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient Tree Cover Estimation
- Author
-
Nazir, Usman, Uppal, Momin, Tahir, Muhammad, and Khalid, Zubair
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Image and Video Processing (eess.IV) ,Computer Science - Computer Vision and Pattern Recognition ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Image and Video Processing ,Machine Learning (cs.LG) - Abstract
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., Comment: IEEE IGARSS 2023
- Published
- 2023
- Full Text
- View/download PDF