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Uncertainty-Aware Graph-Based Multimodal Remote Sensing Detection of Out-Of-Distribution Samples

Having the ability to quantify prediction confidence or uncertainty will greatly assist the successful integration of deep learning methods into high-stake decision making processes. Graph-based convolutional neural networks can be trained to perform classification of multimodal remote sensing data using a model output which represents a Dirichlet distribution parameterization. This parameterization can then also be used to obtain measures of prediction uncertainty. By making a correspondence between a multinomial opinion, as described by subjective logic, and a Dirichlet distribution parameterization, a direct mapping between the two can be performed. A multinomial opinion of this kind can produce quantified measures of uncertainty and distinguish uncertainty due to a lack of evidence (vacuity) and uncertainty due to conflicting evidence (dissonance). With an appropriately chosen loss function, the graph-based classifier will converge to provide accurate estimates of uncertainty. The results presented in this paper show that the measures of uncertainty provided by such models are capable of better distinguishing out-of-distribution data samples than probabilistic measures of uncertainty produced by equivalent deterministic neural networks.