期刊
REMOTE SENSING
卷 13, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/rs13112207
关键词
aircraft detection; remote sensing image; multi-angle; majority voting; convolutional neural network
类别
资金
- National Key Research and Development Program [2017YFB0503600]
- China Geological Survey [DD20191006]
- National Natural Science Foundation of China [41872253]
- Fundamental Research Funds for the Central Universities
Proposed a target detection model based on Faster R-CNN, combining multi-angle feature extraction and majority voting strategy, effectively addressing the challenges of aircraft detection. Achieved higher average precision on public and private datasets by 6.81% and 8.98% respectively.
Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.
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