In order to improve the detection effect of the traditional algorithm on the ground objects in high resolution remote sensing images, this paper applies the deep learning object detection framework Faster R-CNN to the object detection task of high resolution remote sensing images. The airport and aircraft are used as the test scene and detection object for the experiment respectively, The Faster R-CNN framework is trained using the high-resolution remote sensing image data set to obtain the corresponding object detection model. The model is used to detect aircraft objects in high resolution remote sensing images and perform statistical analysis of the experimental results. The experimental results show that the Faster R-CNN model can entirely and accurately detect aircraft objects with an optimal F1 score of 0.976 3, and the same model can be used for object detection of multiple high resolution remote sensing images.
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2019, Vol. 31 
), 张猛
