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. |
| [1] | 张佳, 董守斌. 基于评论方面级用户偏好迁移的跨领域推荐算法 Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer 计算机科学, 2022, 49(9): 41-47. https://doi.org/10.11896/jsjkx.220200131 |
| [2] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [3] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
| [4] | 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210 |
| [5] | 尹文兵, 高戈, 曾邦, 王霄, 陈怡. 基于时频域生成对抗网络的语音增强算法 Speech Enhancement Based on Time-Frequency Domain GAN 计算机科学, 2022, 49(6): 187-192. https://doi.org/10.11896/jsjkx.210500114 |
| [6] | 徐辉, 康金梦, 张加万. 基于特征感知的数字壁画复原方法 Digital Mural Inpainting Method Based on Feature Perception 计算机科学, 2022, 49(6): 217-223. https://doi.org/10.11896/jsjkx.210500105 |
| [7] | 赵小虎, 叶圣, 李晓. 多算法融合的骨骼重建信息动作分类方法 Multi-algorithm Fusion Behavior Classification Method for Body Bone Information Reconstruction 计算机科学, 2022, 49(6): 269-275. https://doi.org/10.11896/jsjkx.210500070 |
| [8] | 高志宇, 王天荆, 汪悦, 沈航, 白光伟. 基于生成对抗网络的5G网络流量预测方法 Traffic Prediction Method for 5G Network Based on Generative Adversarial Network 计算机科学, 2022, 49(4): 321-328. https://doi.org/10.11896/jsjkx.210300240 |
| [9] | 黎思泉, 万永菁, 蒋翠玲. 基于生成对抗网络去影像的多基频估计算法 Multiple Fundamental Frequency Estimation Algorithm Based on Generative Adversarial Networks for Image Removal 计算机科学, 2022, 49(3): 179-184. https://doi.org/10.11896/jsjkx.201200081 |
| [10] | 李建, 郭延明, 于天元, 武与伦, 王翔汉, 老松杨. 基于生成对抗网络的多目标类别对抗样本生成算法 Multi-target Category Adversarial Example Generating Algorithm Based on GAN 计算机科学, 2022, 49(2): 83-91. https://doi.org/10.11896/jsjkx.210800130 |
| [11] | 谈馨悦, 何小海, 王正勇, 罗晓东, 卿粼波. 基于Transformer交叉注意力的文本生成图像技术 Text-to-Image Generation Technology Based on Transformer Cross Attention 计算机科学, 2022, 49(2): 107-115. https://doi.org/10.11896/jsjkx.210600085 |
| [12] | 陈贵强, 何军. 自然场景下遥感图像超分辨率重建算法研究 Study on Super-resolution Reconstruction Algorithm of Remote Sensing Images in Natural Scene 计算机科学, 2022, 49(2): 116-122. https://doi.org/10.11896/jsjkx.210700095 |
| [13] | 石达, 芦天亮, 杜彦辉, 张建岭, 暴雨轩. 基于改进CycleGAN的人脸性别伪造图像生成模型 Generation Model of Gender-forged Face Image Based on Improved CycleGAN 计算机科学, 2022, 49(2): 31-39. https://doi.org/10.11896/jsjkx.210600012 |
| [14] | 唐雨潇, 王斌君. 基于深度生成模型的人脸编辑研究进展 Research Progress of Face Editing Based on Deep Generative Model 计算机科学, 2022, 49(2): 51-61. https://doi.org/10.11896/jsjkx.210400108 |
| [15] | 方仲礼, 王喆, 迟子秋. 面向多标签小样本学习的双流重构网络 Dual-stream Reconstruction Network for Multi-label and Few-shot Learning 计算机科学, 2022, 49(1): 212-218. https://doi.org/10.11896/jsjkx.201100143 |
|
||
首页