Acta Optica Sinica, Volume. 39, Issue 2, 0210001(2019)
Airplane Detection Based on Feature Fusion and Soft Decision in Remote Sensing Images
An airplane detection method is proposed based on feature fusion and soft decision, in which the region-based convolutional neural network is used as the basic framework and the L2 normalization, feature connection, scaling, and dimensionality reduction are in turn used to fuse the multi-layer features. The soft decision, which can improve the traditional non-maximum suppression method, is introduced in order to reduce the detection-omission-rate of grids in the case of significant overlap of targets. The experimental results show that the proposed method can be used to detect airplanes accurately and quickly with a detection rate of 94.25%, a false alarm rate of 5.5%, and the average running time of 0.16 s. Compared with those of the other existing detection methods, each index of the proposed method is significantly improved.
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Mingming Zhu, Yuelei Xu, Shiping Ma, Shuai Li, Hongqiang Ma. Airplane Detection Based on Feature Fusion and Soft Decision in Remote Sensing Images[J]. Acta Optica Sinica, 2019, 39(2): 0210001
Category: Image Processing
Received: Jul. 4, 2018
Accepted: Sep. 11, 2018
Published Online: May. 10, 2019
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