Point process and CNN for small object detection in satellite images

Jules Mabon; Mathias Ortner; Josiane Zerubia

doi:10.1117/12.2635848

26 October 2022 Point process and CNN for small object detection in satellite images

Jules Mabon, Mathias Ortner, Josiane Zerubia

Proceedings Volume 12267, Image and Signal Processing for Remote Sensing XXVIII; 1226707 (2022) https://doi.org/10.1117/12.2635848
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

In this article we present a combination of marked point processes with convolutional neural networks applied to remote sensing. While point processes allow modeling interactions between objects via priors, classical methods rely on contrast measures that become unreliable as objects of interest and context become more diverse. We propose learning likelihood measures using convolutional neural networks to make these measures more versatile and resilient. We apply our method to the detection of vehicles in satellite images.

Citation Download Citation

Jules Mabon, Mathias Ortner, and Josiane Zerubia "Point process and CNN for small object detection in satellite images", Proc. SPIE 12267, Image and Signal Processing for Remote Sensing XXVIII, 1226707 (26 October 2022); https://doi.org/10.1117/12.2635848

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