Abstract
Various generative and discriminative methods have been transferred from the computer vision field to remote sensing applications using different low and high semantic level descriptors. However, as classical approaches have shown their limits in representation learning and are not intended to deal with the great variability of the data. With the emergence of large-scale annotated datasets in vision, the convolutional deep approaches represent the most winning solutions by supporting this variability with spatial context integration through different semantic abstraction levels. In the lack of annotated remote sensing data, in this paper, we are comparing the performances of deep features produced by six different CNNs that have been trained on well established computer vision datasets with respect to the detection of small objects (cars) in very high resolution Pleiades imagery.
Our findings show good generalization performance and are very encouraging for future applications.
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Acknowledgements
This work was supported by NSERC (Engage Grant), the Ministère de l’Économie, des Sciences et de l’Innovation (MESI) of the province of Québec and Effigis Géo Solutions. We are grateful to NVIDIA corporation for the Tesla K40 GPU Hardware Grant to support our work.
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Dahmane, M., Foucher, S., Beaulieu, M., Bouroubi, Y., Benoit, M. (2017). The Potential of Deep Features for Small Object Class Identification in Very High Resolution Remote Sensing Imagery. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_63
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DOI: https://doi.org/10.1007/978-3-319-59876-5_63
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