A Deep Learning Approach to UAV Image Multilabeling

• Published in: IEEE geoscience and remote sensing letters Vol. 14; no. 5; pp. 694 - 698
• Main Authors: Zeggada, Abdallah, Melgani, Farid, Bazi, Yakoub
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-05-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computation; Computer architecture; Convolutional neural networks (CNNs); Deep learning; Exploitation; Feature extraction; Histograms; image multilabeling; Image segmentation; Imagery; Methods; Neural networks; Otsu’s algorithm; Remote sensing; Tiles; Training; Unmanned aerial vehicles; unmanned aerial vehicles (UAVs); urban monitoring
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2017.2671922

In this letter, we face the problem of multilabeling unmanned aerial vehicle (UAV) imagery, typically characterized by a high level of information content, by proposing a novel method based on convolutional neural networks. These are exploited as a means to yield a powerful description of the query image, which is analyzed after subdividing it into a grid of tiles. The multilabel classification task of each tile is performed by the combination of a radial basis function neural network and a multilabeling layer (ML) composed of customized thresholding operations. Experiments conducted on two different UAV image data sets demonstrate the promising capability of the proposed method compared to the state of the art, at the expense of a higher but still contained computation time.