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Detecting and identifying objects in satellite images is a very challengingtask: objects of interest are often very small and features can be difficult torecognize even using very high resolution imagery. For most applications, thistranslates into a trade-off between recall and precision. We present here adedicated method to detect and identify aircraft, combining two very differentconvolutional neural networks (CNNs): a segmentation model, based on a modifiedU-net architecture, and a detection model, based on the RetinaNet architecture.The results we present show that this combination outperforms significantlyeach unitary model, reducing drastically the false negative rate.

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