Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?

Citation

Penatti OAB, Nogueira K & dos Santos JA (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, Boston, MA, USA, 07.06.2015-12.06.2015. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/cvprw.2015.7301382

Abstract
In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed by low-level color descriptors, such as BIC. We also present a correlation analysis, showing the potential for combining/fusing different ConvNets with other descriptors or even for combining multiple ConvNets. A preliminary set of experiments fusing ConvNets obtains state-of-the-art results for the well-known UCMerced dataset.

Keywords
Feature extraction; Image color analysis; Accuracy; Remote sensing; Visualization; Correlation; Histograms

• CVPR version