Feature selection on Sentinel-2 multi-spectral imagery for efficient tree cover estimation

Conference item

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.

Authors: Nazir, U; Uppal, M; Tahir, M; Khalid, Z

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
• Pages: 2946-2949
• Publication date: 2023-10-20
• Event title: 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023)
• Event location: Pasadena, California, USA
• Event website: https://2023.ieeeigarss.org/
• Event start date: 2023-07-16
• Event end date: 2023-07-21
• DOI: 10.1109/IGARSS52108.2023.10283235
• EISSN: 2153-7003
• ISSN: 2153-6996
• EISBN: 9798350320107
• ISBN: 9798350331745
• Language: English
• Keywords: image resolution; European Space Agency; urban areas; estimation; deep architecture; geoscience and remote sensing; feature extraction