Expanding SAR-based probabilistic deforestation detections using deep learning

Alice M. S. Durieux; Rose Rustowicz; Nikhil Sharma; Jason Schatz; Matthew T. Calef; Christopher X. Ren

doi:10.1117/12.2594031

1 August 2021 Expanding SAR-based probabilistic deforestation detections using deep learning

Alice M. S. Durieux, Rose Rustowicz, Nikhil Sharma, Jason Schatz, Matthew T. Calef, Christopher X. Ren

Proceedings Volume 11843, Applications of Machine Learning 2021; 1184307 (2021) https://doi.org/10.1117/12.2594031
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

In this work, we leverage deep learning to reproduce and expand Synthetic Aperture Radar (SAR) based deforestation detections generated using a probabilistic Bayesian model. Our Bayesian updating deforestation detections leverage SAR backscatter and InSAR coherence to perform change detection on forested areas and detect deforestation regardless of cloud cover. However, this model does not capture all deforestation events and is better suited to near-real time alerting than accurate forest loss acreage estimates. Here, we use SAR based probabilistic detections as deforested labels and Sentinel-2 optical composites as input features to train a neural network to differentiate deforested patches at various stages of regrowth from native forest. The deep learning model predictions demonstrate excellent recall of the original Bayesian label, and low precision due to providing better coverage of deforestation and detecting deforested patches not included in the imperfect Bayesian labels. These results provide an avenue to improve existing deforestation models, specifically with regards to their ability to quantify deforested acreage.

Conference Presentation

Citation Download Citation

Alice M. S. Durieux, Rose Rustowicz, Nikhil Sharma, Jason Schatz, Matthew T. Calef, and Christopher X. Ren "Expanding SAR-based probabilistic deforestation detections using deep learning", Proc. SPIE 11843, Applications of Machine Learning 2021, 1184307 (1 August 2021); https://doi.org/10.1117/12.2594031

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