Abstract
Abstract. Deforestation in the Amazon rainforest is an alarming problem of global interest. Environmental impacts of this process are countless, but probably the most significant concerns regard the increase in CO2 emissions and global temperature rise. Currently, the assessment of deforested areas in the Amazon region is a manual task, where people analyse multiple satellite images to quantify the deforestation. We propose a method for automatic deforestation detection based on Deep Learning Neural Networks with dual-attention mechanisms. We employed a siamese architecture to detect deforestation changes between optical images in 2018 and 2019. Experiments were performed to evaluate the relevance and sensitivity of hyperparameter tuning of the loss function and the effects of dual-attention mechanisms (spatial and channel) in predicting deforestation. Experimental results suggest that a proper tuning of the loss function might bring benefits in terms of generalisation. We also show that the spatial attention mechanism is more relevant for deforestation detection than the channel attention mechanism. When both mechanisms are combined, the greatest improvements are found, and we reported an increase of 1.06% in the mean average precision over a baseline.
Highlights
The environmental impacts of deforestation in the Amazon rainforest have been attracting research interest for many decades (Lean, Warrilow, 1989, Shukla et al, 1990)
This paper reported the application of a deep-learning network, equipped with a dual-attention mechanism, to the task of deforestation detection in the Amazon rainforest
A set of experiments were implemented to analyse the effects of both margins and weights of the Weighted Double Margin Contrastive (WDMC) loss function in the prediction of deforestation
Summary
The environmental impacts of deforestation in the Amazon rainforest have been attracting research interest for many decades (Lean, Warrilow, 1989, Shukla et al, 1990). Given the importance of deforestation detection in the Amazon rainforest, methods based on DL have been applied to this problem aiming to provide a robust and automatic way to monitor Amazon deforested areas (Ortega Adarme et al, 2020, de Bem et al, 2020). The methodology’s main idea is to use dual-attention mechanism (spatial and channel) to improve the robustness against pseudo-changes in remote sensing applications, i.e., efficiently distinguishing between relevant changes and circumstantial ones, such as noise and context This concept, which will be described in details has great potential for deforestation detection, and in this case, the relevant change is current deforestation itself.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
