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