  In semantic segmentation knowing about all existing classes is essential to
yield effective results with the majority of existing approaches. However,
these methods trained in a Closed Set of classes fail when new classes are
found in the test phase. It means that they are not suitable for Open Set
scenarios, which are very common in real-world computer vision and remote
sensing applications. In this paper, we discuss the limitations of Closed Set
segmentation and propose two fully convolutional approaches to effectively
address Open Set semantic segmentation: OpenFCN and OpenPCS. OpenFCN is based
on the well-known OpenMax algorithm, configuring a new application of this
approach in segmentation settings. OpenPCS is a fully novel approach based on
feature-space from DNN activations that serve as features for computing PCA and
multi-variate gaussian likelihood in a lower dimensional space. Experiments
were conducted on the well-known Vaihingen and Potsdam segmentation datasets.
OpenFCN showed little-to-no improvement when compared to the simpler and much
more time efficient SoftMax thresholding, while being between some orders of
magnitude slower. OpenPCS achieved promising results in almost all experiments
by overcoming both OpenFCN and SoftMax thresholding. OpenPCS is also a
reasonable compromise between the runtime performances of the extremely fast
SoftMax thresholding and the extremely slow OpenFCN, being close able to run
close to real-time. Experiments also indicate that OpenPCS is effective, robust
and suitable for Open Set segmentation, being able to improve the recognition
of unknown class pixels without reducing the accuracy on the known class
pixels.
