An Advanced Dirichlet Prior Network for Out-of-Distribution Detection in Remote Sensing

Remote sensing deals with a plethora of sensors, a large number of classes/categories, and a huge variation in geography. Due to the difficulty of collecting labeled data uniformly representing all scenarios, data-hungry deep learning models are often trained with labeled data in a source domain that is limited in the above-mentioned aspects. However, during the test/inference phase, such deep learning models are often subjected to a distributional shift, also called out-of-distribution (OOD) samples, in the form of unseen classes, geographic differences, and multisensor differences. Deep learning models can behave in an unexpected manner when subjected to such distributional uncertainties. Vulnerability to OOD data severely reduces the reliability of deep learning models and trusting on such predictions in the absence of any reliability indicator may lead to wrong policy decisions or mishaps in time-bound remote sensing applications. Motivated by this, in this work, we propose a Dirichlet prior network-based model to quantify the distributional uncertainty of deep learning-based remote sensing models. The approach seeks to maximize the representation gap between the in-domain and OOD examples for better segregation of OOD samples at test time. Extensive experiments on several remote sensing image classification datasets demonstrate that the proposed model can quantify distributional uncertainty. To the best of our knowledge, this is the first work to elaborately study distributional uncertainty in context of remote sensing. The codes are publicly available at https://gitlab.lrz.de/ai4eo/Uncertainty/-/tree/main/DPN-RS.