FUSING SENTINEL-1 AND SENTINEL-2 IMAGES WITH TRANSFORMER-BASED NETWORK FOR DEFORESTATION DETECTION IN THE BRAZILIAN AMAZON UNDER DIVERSE CLOUD CONDITIONS

Abstract

Deforestation is an environmental problem that significantly impacts biodiversity and climate change. Deforestation detection is usually performed using optical remote sensing images, limiting the detection capability to the dry season in which images are not comprised of clouds. In this work, we proposed Transformer-based models to fuse bitemporal Sentinel-1 and Sentinel-2 images to identify new deforestation areas in the Brazilian Amazon area under diverse cloud conditions. The models were evaluated considering clear and cloud-covered pixel conditions. The results confirmed previous works in which the fusion of optical and SAR images improved deforestation detection capabilities. We also concluded that the better Transformer-based network reached the F1-Score of 0.92, considering all pixels, outperforming the better Convolution-based which reached the F1-Score of 0.86, without increasing the training and prediction times.