Comparison of Optical and SAR Data for Deforestation Mapping in the Amazon Rainforest with Fully Convolutional Networks

• Published in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS pp. 3769 - 3772
• Main Authors: Ortega, Mabel X., Feitosa, Raul Q., Bermudez, Jose D., Happ, Patrick N., De Almeida, Claudio A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11-07-2021
• Subjects: Adaptive optics; Convolutional codes; deep leaning; Deforestation; fully convolutional networks; Optical fiber networks; Optical imaging; Radar remote sensing; Speckle; synthetic aperture radar
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS47720.2021.9554970

Early detection of deforestation processes is vital to maintain and regulate tropical rainforests, such as in the Amazon region. Most of them rely on optical imagery. Approaches based on Synthetic Aperture Radar (SAR) data are comparatively unexplored, in particular for deforestation detection in tropical rainforests. This work addresses this gap and evaluates Fully Convolutional Networks based on the U-Net, Res-Unet and Siamese Network, for deforestation detection using images from three different sensors, Landsat-8, Sentinel-2, and Sentinel-1. Experiments conducted on a dataset of the Amazon rainforest indicated that Fully Convolutional Networks working on Sentinel-1 data can achieve sufficient accuracy for detecting deforestation in tropical rainforests when clouds prevent the use of optical data 1 1 The source code is available in https://github.zcom/MabelOrtega/Comparison-of-Optical-and-SAR-data-for-deforestation-mapping-in-the-Amazon-Forest-with-FCN.