  With its vast expanse, exceeding that of Western Europe by twice, the Amazon
rainforest stands as the largest forest of the Earth, holding immense
importance in global climate regulation. Yet, deforestation detection from
remote sensing data in this region poses a critical challenge, often hindered
by the persistent cloud cover that obscures optical satellite data for much of
the year. Addressing this need, this paper proposes three deep-learning models
tailored for deforestation monitoring, utilizing SAR (Synthetic Aperture Radar)
multitemporal data moved by its independence on atmospheric conditions.
Specifically, the study proposes three novel recurrent fully convolutional
network architectures-namely, RRCNN-1, RRCNN-2, and RRCNN-3, crafted to enhance
the accuracy of deforestation detection. Additionally, this research explores
replacing a bitemporal with multitemporal SAR sequences, motivated by the
hypothesis that deforestation signs quickly fade in SAR images over time. A
comprehensive assessment of the proposed approaches was conducted using a
Sentinel-1 multitemporal sequence from a sample site in the Brazilian
rainforest. The experimental analysis confirmed that analyzing a sequence of
SAR images over an observation period can reveal deforestation spots
undetectable in a pair of images. Notably, experimental results underscored the
superiority of the multitemporal approach, yielding approximately a five
percent enhancement in F1-Score across all tested network architectures.
Particularly the RRCNN-1 achieved the highest accuracy and also boasted half
the processing time of its closest counterpart.

Computer Vision and Pattern Recognition;Machine Learning