Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images

• URL: http://arxiv.org/abs/2005.13215v1
• Date: Wed, 27 May 2020 07:35:55 GMT
• Title: Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images
• Authors: Damien Grosgeorge (SAS), Maxime Arbelot (SAS), Alex Goupilleau (SAS), Tugdual Ceillier (SAS), Renaud Allioux (SAS)
• Abstract summary: We present a dedicated method to detect and identify aircraft, combining two very different convolutional neural networks (CNNs) The results we present show that this combination outperforms significantly each unitary model, reducing drastically the false negative rate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting and identifying objects in satellite images is a very challenging task: objects of interest are often very small and features can be difficult to recognize even using very high resolution imagery. For most applications, this translates into a trade-off between recall and precision. We present here a dedicated method to detect and identify aircraft, combining two very different convolutional neural networks (CNNs): a segmentation model, based on a modified U-net architecture, and a detection model, based on the RetinaNet architecture. The results we present show that this combination outperforms significantly each unitary model, reducing drastically the false negative rate.

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