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Due to their ability to offer more comprehensive information than data from asingle view, multi-view (multi-source, multi-modal, multi-perspective, etc.)data are being used more frequently in remote sensing tasks. However, as thenumber of views grows, the issue of data quality becomes more apparent,limiting the potential benefits of multi-view data. Although recent deep neuralnetwork (DNN) based models can learn the weight of data adaptively, a lack ofresearch on explicitly quantifying the data quality of each view when fusingthem renders these models inexplicable, performing unsatisfactorily andinflexible in downstream remote sensing tasks. To fill this gap, in this paper,evidential deep learning is introduced to the task of aerial-ground dual-viewremote sensing scene classification to model the credibility of each view.Specifically, the theory of evidence is used to calculate an uncertainty valuewhich describes the decision-making risk of each view. Based on thisuncertainty, a novel decision-level fusion strategy is proposed to ensure thatthe view with lower risk obtains more weight, making the classification morecredible. On two well-known, publicly available datasets of aerial-grounddual-view remote sensing images, the proposed approach achievesstate-of-the-art results, demonstrating its effectiveness. The code anddatasets of this article are available at the following address:https://github.com/gaopiaoliang/Evidential.

Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images