  Classical and more recently deep computer vision methods are optimized for
visible spectrum images, commonly encoded in grayscale or RGB colorspaces
acquired from smartphones or cameras. A more uncommon source of images
exploited in the remote sensing field are satellite and aerial images. However,
the development of pattern recognition approaches for these data is relatively
recent, mainly due to the limited availability of this type of images, as until
recently they were used exclusively for military purposes. Access to aerial
imagery, including spectral information, has been increasing mainly due to the
low cost of drones, cheapening of imaging satellite launch costs, and novel
public datasets. Usually remote sensing applications employ computer vision
techniques strictly modeled for classification tasks in closed set scenarios.
However, real-world tasks rarely fit into closed set contexts, frequently
presenting previously unknown classes, characterizing them as open set
scenarios. Focusing on this problem, this is the first paper to study and
develop semantic segmentation techniques for open set scenarios applied to
remote sensing images. The main contributions of this paper are: 1) a
discussion of related works in open set semantic segmentation, showing evidence
that these techniques can be adapted for open set remote sensing tasks; 2) the
development and evaluation of a novel approach for open set semantic
segmentation. Our method yielded competitive results when compared to closed
set methods for the same dataset.
