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

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 6; p. 1546
• Main Authors: Zhao, Kun, Gao, Qian, Hao, Siyuan, Sun, Jie, Zhou, Lijian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2023
• Subjects: Artificial intelligence; Artificial neural networks; Classification; Comparative analysis; Credibility; Data fusion; Datasets; Decision making; Deep learning; Dirichlet distribution; evidential learning; Image processing; Land use; Machine learning; Methods; multi-view data fusion; Neural networks; Remote sensing; remote sensing scene classification; Technology application; Uncertainty; uncertainty estimation; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15061546

Due to their ability to offer more comprehensive information than data from a single view, multi-view (e.g., multi-source, multi-modal, multi-perspective) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality is becoming 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 inflexibly 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.