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Active Fire Detection in Multispectral Super-Resolved Sentinel-2 Images by Means of Sam-Based Approach

In the last years, Sentinel-2 data have become extensively used by the remote sensing community due to their relatively fine spatial resolution, high revisit time ensured by the twin satellites Sentinel- 2 and, of course, their free availability. However, not all the bands are provided at the highest resolution (10 meters). For instance, the Short-Wave Infrared (SWIR) bands, very useful for fires monitoring applications, are provided at 20 meters. Therefore, in order to have a more detailed Active Fire maps, we have proposed a super-resolution data fusion method based on Convolutional Neural Network (CNN), hereafter SRNN+. Then we have compared the standard Active Fire Detection (AFDs) based on indices (AFIs) [1], widely used in literature for active fires monitoring purpose, with a method based on the Spectral Angular Mapper (SAM) [2]. The proposed analysis is validated on the widespread fires that damaged the volcano Vesuvius (Italy) during the summer of 2017.