论文标题
在卫星图像中用于飞机检测和识别的同时分割和对象检测CNN
Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images
论文作者
论文摘要
在卫星图像中检测和识别对象是一项非常具有挑战性的任务：感兴趣的对象通常很小，即使使用很高的分辨率图像也很难识别功能。对于大多数应用程序，这转化为召回和精确度之间的权衡。我们在这里提出了一种专门的方法来检测和识别飞机，结合了两个非常不同的卷积神经网络（CNN）：基于修改的U-NET体系结构和基于视网膜体系结构的检测模型的分割模型。我们提出的结果表明，这种组合的表现明显优于每个单一模型，从而大大降低了假负率。
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.
