Abstract
The automatic detection and identification of buildings has always been a hotspot research in the field of remote sensing image processing. In recent years, unmanned aerial vehicles (UAV) have developed rapidly and provided high-resolution remote sensing images to detect and identify surface targets. At the same time, the deep learning method has achieved great success in the fields of speech recognition, image recognition, information retrieval, etc., becoming an effective tool for classification and recognition. This paper presents the method of building recognition by deep learning for UAV remote sensing. The Faster RCNN model is applied to identify the UAV remote sensing images, the experiments show that the recognition accuracy is 93.2% for this dataset with an average processing time of 74ms on the image recognition. The results suggest the effectiveness and efficiency of the building recognition applications of UAV remote sensing images by deep learning network.
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