  Land cover classification of satellite imagery is an important step toward
analyzing the Earth's surface. Existing models assume a closed-set setting
where both the training and testing classes belong to the same label set.
However, due to the unique characteristics of satellite imagery with an
extremely vast area of versatile cover materials, the training data are bound
to be non-representative. In this paper, we study the problem of open-set land
cover classification that identifies the samples belonging to unknown classes
during testing, while maintaining performance on known classes. Although
inherently a classification problem, both representative and discriminative
aspects of data need to be exploited in order to better distinguish unknown
classes from known. We propose a representative-discriminative open-set
recognition (RDOSR) framework, which 1) projects data from the raw image space
to the embedding feature space that facilitates differentiating similar
classes, and further 2) enhances both the representative and discriminative
capacity through transformation to a so-called abundance space. Experiments on
multiple satellite benchmarks demonstrate the effectiveness of the proposed
method. We also show the generality of the proposed approach by achieving
promising results on open-set classification tasks using RGB images.
