计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 93-99.doi: 10.11896/jsjkx.200700109
所属专题: 复杂系统的软件工程和需求工程
徐永士, 贲可荣, 王天雨, 刘斯杰
XU Yong-shi, BEN Ke-rong, WANG Tian-yu, LIU Si-jie
摘要: 针对SAR图像识别软件通过改进DCGAN模型单生成器与单判别器对抗的结构采用多生成器与单判别器进行对抗设计了控制各生成器生成图像平均质量的算法提出了一种基于改进的DCGAN生成SAR图像的方法.为测试和验证多个同类图像识别软件并进行择优需要自行设计不同于训练用的图像来对测软件进行测试.此方法可以为择优测试提供一个公平的基准测试集.实验分别使用原DCGAN模型和改进的DCGAN模型生成目标图像和场景图像并使用公开判别器分别对两种模型生成的新图像进行质量验证.实验结果表明改进的DCGAN模型比原DCGAN模型生成的图像效果更好经其训练生成的新SAR图像与原SAR图像相比质量相当且多样性更好可以满足软件择优测试的需要.
中图分类号:
[1] SPENCER JR B F,HOSKERE V,NARAZAKI Y.Advances in computer vision-based civil infrastructure inspection and monitoring[J].Engineering,2019,5(2):199-222. [2] LIN J,LI X,PAN H.Aircraft recognition in remote sensing images based on deep learning[C]//Proceedings of 33rd Youth Academic Annual Conference of Chinese Association of Automation(YAC).Nanjing:IEEE,2018:895-899. [3] YAGHOOBI H,MANSOURI H,FARSANGI M A E,et al.Determining the fragmented rock size distribution using textural feature extraction of images[J].Powder Technology ,2019,34(2):630-641. [4] RAUL O P.Using TensorFlow-based neural network to estimate GNSS single frequency ionospheric delay[J].Advances in Space Research,2019,63(5):1607-1618. [5] LOU M Y,LIU Y Q,YANG F,et al.Image enhancement of palm veins based on adaptive fusion and Gabor filter[C]//Proceedings of the 5nd Fuzzy Systems and Data Mining.Kitakyushu City,Japan.IOS Press.2019:296-304. [6] MOHAMED A A,BERG W A,PENG H,et al.A deep learning method for classifying mammographic breast density categories[J].Medical Physics,2018,45(1):314-321. [7] YAN Q,GONG D,ZHANG Y.Two-Stream Convolutional Networks for Blind Image Quality Assessment[J].IEEE Transactions on Image Processing,2019,28(5):2200-2211. [8] WANG Y C,GAO J H.Test Case Assignment and Selection Method Based on Run Profile[J].Computer Engineering,2020,46(6):216-220. [9] OLGA R,JIA D,HAO S,et al.ImageNet Large Scale Visual Recognition Challenge[J].International Journal of Computer Vision,2015,115(3):211-252. [10] FEI G,TENG H,JIN S,et al.A New Algorithm of SAR Image Target Recognition Based on Improved Deep Convolutional Neural Network[J].Cognitive Computation,2019,11 (6):809-824. [11] DING B Y,WEN G J,YU L S,et al.Matching of attributedscattering center and its application to synthetic aperture radar Automatic Target Recognition[J].Journal of Radar,2017,6(2):157-166. [12] LI G L,MA Y F.Air Combat Situation Feature ExtractionBased on Deep Network[J].Journal of System Simulation,2017,29(S1):98-105,112. [13] MIAO S,LIU X.Joint sparse representation of complementary components in SAR images for robust target recognition[J].Journal of Electromagnetic Waves and Applications,2019,33(7):882-896. [14] CASTELLAZZI P,DOODY T,PEETERS L.Towards monitoring groundwater-dependent ecosystems using synthetic aperture radar imagery[J].Hydrological Processes,2019,33 (25):3239-3250. [15] IAN G,JEAN P,MEHDI M,et al.Generative Adversarial Nets[C]//ACM.The 27th International Conference on Neural Information Processing Systems.Canada.New York:ACM,2014:2672-2680. [16] LILLIAN J R,SAMUEL A B,SHANKAR S.Characterization and computation of local Nash equilibria in continuous games[C]//Proceedings of the 51st Annual Allerton Conference on Communication,Control,and Computing (Allerton).Monticello,IL,USA:IEEE,2013:917-924. [17] MIRZA M,OSINDERO S.Conditional generative adversarialnets[J].Computer Science,2014,27(8):2672-2680. [18] VAN O A,KALCHBRENNER N,ESPEHOLT L.Conditionalimage generation with Pixel CNN decoders[C]//Proceedings of Advances in Neural Information Processing Systems.Barce-lona,Spain:Curran Associates,Inc.,2016:4790-4798. [19] SCOTT R,ZEYNEP A,SANTOSH M,et al.Learning what and where to draw[C]//Proceedings of Advances in Neural Information Processing Systems.Barcelona,Spain:Curran Associates,Inc.,2016:217-225. [20] ZHANG L,ZHAO J Y,YE X L,et al.Collaborative Generation of Confrontation Networks[J].Acta Automatica Sinica,2018,44(5):804-810. [21] RADFORD A,METZ L,CHINTALA S.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J].arXiv:1511.06434. [22] ZHU L,CHEN Y,GHAMISI P,et al.Generative adversarial networks for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(9):5046-5063. |
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