基于特征融合与软判决的遥感图像飞机检测
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摘要
提出了一种特征融合结合软判决的飞机检测方法。以区域卷积神经网络为基本框架,依次采用L2范数归一化、特征连接、尺度缩放和特征降维来融合多层特征。为了降低网络在目标高度重叠时的漏检率,引入软判决来改进传统的非极大值抑制方法。实验结果表明,所提方法能够准确快速地检测到飞机,得到检测率为94.25%、虚警率为5.5%、平均运行时间为0.16 s的实验结果。与现有的其他检测方法相比,所提方法的各项指标均得到显著提升。
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.
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.
引文
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[2] Li W, Xiang S M, Wang H B, et al. Robust airplane detection in satellite images[C]. IEEE International Conference on Image Processing, 2011: 2821-2824.
[3] Bo S K, Jing Y J. Region-based airplane detection in remotely sensed imagery[C]. International Congress on Image and Signal Processing, 2010: 1923-1926.
[4] Liu L, Shi Z W. Airplane detection based on rotation invariant and sparse coding in remote sensing images[J]. Optik-International Journal for Light and Electron Optics, 2014, 125(18): 5327-5333.
[5] Chen X Y, Xiang S M, Liu C L, et al. Aircraft detection by deep belief nets[C]. Asian Conference on Pattern Recognition, 2013: 54-58.
[6] Wu H, Zhang H, Zhang J F, et al. Fast aircraft detection in satellite images based on convolutional neural networks[C]. IEEE International Conference on Image Processing, 2015: 4210-4214.
[7] Xin P, Xu Y L, Tan H, et al. Fast airplane detection based on multi-layer feature fusion of fully convolutional networks[J]. Acta Optica Sinica, 2018, 38(3): 0315003. 辛鹏, 许悦雷, 唐红, 等. 全卷积网络多层特征融合的飞机快速检测[J]. 光学学报, 2018, 38(3): 0315003.
[8] Li Y, Fu K, Sun H, et al. An aircraft detection framework based on reinforcement learning and convolutional neural networks in remote sensing images[J]. Remote Sensing, 2018, 10(2): 243.
[9] Zhang W C, Sun X, Fu K, et al. Object detection in high-resolution remote sensing images using rotation invariant parts based model[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 74-78.
[10] Wang G L, Wang X C, Fan B, et al. Feature extraction by rotation-invariant matrix representation for object detection in aerial image[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 851-855.
[11] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359.
[12] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[13] Girshick R B. Fast R-CNN[C]. International Conference on Computer Vision, 2015: 1440-1448.
[14] Kong T, Yao A, Chen Y, et al. HyperNet: towards accurate region proposal generation and joint object detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 845-853.
[15] Liu W, Rabinovich A, Berg A C, et al. ParseNet: looking wider to see better[EB/OL]. (2015-11-23)[2018-01-15]. https://arxiv.org/abs/1506.04579
[16] Rothe R, Guillaumin M, Van Gool L, et al. Non-maximum suppression for object detection by passing messages between windows[C]. Asian Conference on Computer Vision, 2014: 290-306.