  In satellite image analysis, distributional mismatch between the training and
test data may arise due to several reasons, including unseen classes in the
test data and differences in the geographic area. Deep learning based models
may behave in unexpected manner when subjected to test data that has such
distributional shifts from the training data, also called out-of-distribution
(OOD) examples. Predictive uncertainly analysis is an emerging research topic
which has not been explored much in context of satellite image analysis.
Towards this, we adopt a Dirichlet Prior Network based model to quantify
distributional uncertainty of deep learning models for remote sensing. The
approach seeks to maximize the representation gap between the in-domain and OOD
examples for a better identification of unknown examples at test time.
Experimental results on three exemplary test scenarios show the efficacy of the
model in satellite image analysis.
