Fusing Sentinel-1 and Sentinel-2 Images for Deforestation Detection in the Brazilian Amazon Under Diverse Cloud Conditions

Most of the current deforestation detection systems rely on cloud-free optical images, which are difficult to obtain in tropical regions. A synthetic aperture radar (SAR) is nearly unaffected by clouds, thus providing valuable insights for deforestation detection. In cloud-free conditions, the use of optical images usually provides better results than the use of SAR data alone. Optical-SAR fusion has been hailed as a promising way to improve deforestation detection. However, it was poorly investigated, particularly when optical images are affected by clouds. This letter employs optical-SAR fusion strategies to improve the classification accuracy of clear-cut deforestation in the Brazilian Amazon under diverse cloud conditions. Sentinel-1 and Sentinel-2 images were fused using fully convolutional networks (FCNs) and early, joint, and late fusion (LF) strategies. Experiments showed that the optical-SAR fusion outperforms the single-modality (optical or SAR) variants for deforestation detection on pixels affected by clouds. The joint fusion strategy provided the best results in all cloud cover scenarios.