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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 [1], and a detection model, based on the RetinaNet architecture [2]. The results we present show that this combination outperforms significantly each unitary model, reducing drastically the false negative rate.