Out-of-distribution detection in satellite image classification
This addresses distributional shifts in satellite imagery for remote sensing applications, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of out-of-distribution detection in satellite image classification by adopting a Dirichlet Prior Network model to quantify uncertainty, showing efficacy in three test scenarios.
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.