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

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 tr...

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Bibliographic Details
Published in2021 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
LanguageEnglish
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
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Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS47720.2021.9554970