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基于模糊不变卷积神经网络的遥感飞机识别

Remote Sensing Aircraft Recognition Based on Blur-Invariant Convolutional Neural Network

刘坤   苏彤   王典  
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摘要

提出了一种基于模糊不变卷积神经网络(BICNN)模型的目标识别方法。与传统卷积神经网络(CNN)模型不同,BICNN引入了一种新的模糊不变层。 BICNN通过增加模糊不变约束项及正则化来优化模糊不变目标函数并进行训练; 通过减小模糊不变目标函数值,使得训练样本在模糊前后的特征映射一致,最终实现模糊不变性。测试结果表明,BICNN解决了模糊造成的识别率低的问题,增大了运动模糊图像的识别率。

Abstract

A method of target recognition based on the blur-invariant convolutional neural network (BICNN) model is proposed. The BICNN model introduces a new blur-invariant layer, which is different from the traditional convolutional neural network (CNN )models. BICNN is trained by the adding of the blur-invariant constraint term and the regularization to optimize a blur-invariant objective function. The value of the fuzzy invariant objective function is reduced to make the training samples consistent with the feature maps before and after the blurring, and thus the blur invariance is achieved finally. The test results show that BICNN can solve the problem of a low recognition rate caused by blur and improve the recognition rate of the motion blurred images.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.4

DOI:10.3788/lop55.082001

所属栏目:光计算

基金项目:国家自然科学基金(61271446)、航空科学基金(2013ZC15005)

收稿日期:2018-02-07

修改稿日期:2018-02-16

网络出版日期:2018-03-01

作者单位    点击查看

刘坤:上海海事大学信息工程学院, 上海 200135
苏彤:上海海事大学信息工程学院, 上海 200135
王典:上海海事大学信息工程学院, 上海 200135

联系人作者:苏彤(sutong@stu.shmtu.edu.cn)

【1】Simonyan K, Parkhi O M, Vedaldi A, et al. Fisher vector faces in the wild[C]. Proceedings of the British Machine Vision Conference, 2013: 8.1-8.11.

【2】Nishiyama M, Hadid A, Takeshima H, et al. Facial deblur inference using subspace analysis for recognition of blurred faces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(4): 838-845.

【3】Yang S J, Ye X, Zhang S J. A new infrared turbulent fuzzy image restoration algorithm based on Gaussian function parameter identification[C]. International Conference on Image, Vision and Computing, 2017: 423-427.

【4】Zhang H, Shu H, Han G N, et al. Blurred image recognition by Legendre moment invariants[J]. IEEE Transactions on Image Processing, 2010, 19(3): 596-611.

【5】Dai X B, Liu T L. Image recognition algorithm by blur invariants of pseudo-Zernike moment[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science), 2013, 33(3): 50-55.
戴修斌, 刘天亮. 基于伪Zernike矩模糊不变量的图像识别算法[J]. 南京邮电大学学报(自然科学版), 2013, 33(3): 50-55.

【6】He K, Zhang X, Ren S, et al. Image net classification with deep convolutional neural networks[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.

【7】Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.
刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4): 0428001.

【8】Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-04)[2017-12-10]. https:∥arxiv.org/abs/1409.1556.

【9】Xie X K. Research on blurred image recognition based on transfer learning[D]. Wuhan: Huazhong University of Science and Technology, 2016.
解晓康. 基于迁移学习的模糊图像识别技术研究[D]. 武汉: 华中科技大学, 2016.

【10】Wang W, Cao Z. Recognition of blurred faces using local phase pattern[J]. Electronics Letters, 2012, 48(20): 1269-1271.

【11】Liu F, Shen T S, Ma X X. Convolutional neural network based multi-band ship target recognition with feature fusion[J]. Acta Optica Sinica, 2017, 37(10): 1015002.
刘峰, 沈同圣, 马新星. 特征融合的卷积神经网络多波段舰船目标识别[J]. 光学学报, 2017, 37(10): 1015002.

【12】Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 815-823.

【13】Ye G L, Sun S Y, Gao K J, et al. Nighttime pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(8): 081003.
叶国林, 孙韶媛, 高凯珺, 等. 基于加速区域卷积神经网络的夜间行人检测研究[J]. 激光与光电子学进展, 2017, 54(8): 081003.

【14】Taigman Y, Yang M, Ranzato M A, et al. DeepFace: Closing the gap to human-level performance in face verification[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1701-1708.

【15】Wang J, Yuan C. Facial expression recognition with multi-scale convolution neural network[C]. Pacific Rim Conference on Multimedia, 2016: 376-385.

【16】Richardson W H. Bayesian-based iterative method of image restoration[J]. Journal of the Optical Society of America,1972, 62(1): 55-59.

【17】Ojansivu V, Heikkil J. Blur insensitive texture classication using local phase quantization[C]. International Conference on Image and Signal Processing, 2008, 5099: 236-243.

【18】Chen Y, Fan R S, Wang J X, et al. High resolution image classification method combining with minimum noise fraction rotation and convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(10): 102801.
陈洋, 范荣双, 王竞雪, 等. 结合最小噪声分离变换和卷积神经网络的高分辨影像分类方法[J]. 激光与光电子学进展, 2017, 54(10): 102801.

【19】Yu D, Deng L. Deep learning and its applications to signal and information processing[J]. IEEE Signal Processing Magazine, 2011, 28(1): 145-154.

【20】Ji S, Xu W, Yang M, et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 221-231.

【21】Hu F, Xia G S, Hu J, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015, 7(11): 14680-14707.

引用该论文

Liu Kun,Su Tong,Wang Dian. Remote Sensing Aircraft Recognition Based on Blur-Invariant Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(8): 082001

刘坤,苏彤,王典. 基于模糊不变卷积神经网络的遥感飞机识别[J]. 激光与光电子学进展, 2018, 55(8): 082001

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