INVESTIGATING SAR-OPTICAL DEEP LEARNING DATA FUSION TO MAP THE BRAZILIAN CERRADO VEGETATION WITH SENTINEL DATA

Despite its environmental and societal importance, accurately mapping the Brazilian Cerrado's vegetation is still an open challenge. Its diverse but spectrally similar physiognomies are difficult to be identified and mapped by state-of-the-art methods from only medium- to high-resolution optical images. This work investigates the fusion of Synthetic Aperture Radar (SAR) and optical data in convolutional neural network architectures to map the Cerrado according to a 2-level class hierarchy. Additionally, the proposed model is designed to deal with uncertainties that are brought by the difference in resolution between the input images (at 10m) and the reference data (at 30m). We tested four data fusion strategies and showed that the position for the data combination is important for the network to learn better features.