光学学报, 2018, 38 (1): 0111005, 网络出版: 2018-08-31   

基于认知模型的遥感图像有效飞机检测系统 下载: 840次

Valid Aircraft Detection System for Remote Sensing Images Based on Cognitive Models
作者单位
空军航空大学数字地球实验室, 吉林 长春 130000
引用该论文

侯宇青阳, 全吉成, 魏湧明. 基于认知模型的遥感图像有效飞机检测系统[J]. 光学学报, 2018, 38(1): 0111005.

Yuqingyang Hou, Jicheng Quan, Yongming Wei. Valid Aircraft Detection System for Remote Sensing Images Based on Cognitive Models[J]. Acta Optica Sinica, 2018, 38(1): 0111005.

参考文献

[1] 徐大琦, 倪国强, 许廷发. 中高分辨力遥感图像中飞机目标自动识别算法研究[J]. 光学技术, 2006, 32(6): 855-862.

    徐大琦, 倪国强, 许廷发. 中高分辨力遥感图像中飞机目标自动识别算法研究[J]. 光学技术, 2006, 32(6): 855-862.

    徐大琦, 倪国强, 许廷发. 中高分辨力遥感图像中飞机目标自动识别算法研究[J]. 光学技术, 2006, 32(6): 855-862.

    Xu D Q, Ni G Q, Xu T F. Study on the algorithm for automatic plane classification from remote sensing images with min-high resolution[J]. Optical Technique, 2006, 32(6): 855-862.

    Xu D Q, Ni G Q, Xu T F. Study on the algorithm for automatic plane classification from remote sensing images with min-high resolution[J]. Optical Technique, 2006, 32(6): 855-862.

    Xu D Q, Ni G Q, Xu T F. Study on the algorithm for automatic plane classification from remote sensing images with min-high resolution[J]. Optical Technique, 2006, 32(6): 855-862.

[2] 岳伍军. 基于光学遥感图像的飞机目标检测算法研究[D]. 成都: 西南交通大学, 2014.

    岳伍军. 基于光学遥感图像的飞机目标检测算法研究[D]. 成都: 西南交通大学, 2014.

    岳伍军. 基于光学遥感图像的飞机目标检测算法研究[D]. 成都: 西南交通大学, 2014.

    Yue WJ. Research of aircraft target detection algorithm based on optical remote sensing image[D]. Chengdu: Southwest Jiaotong University, 2014.

    Yue WJ. Research of aircraft target detection algorithm based on optical remote sensing image[D]. Chengdu: Southwest Jiaotong University, 2014.

    Yue WJ. Research of aircraft target detection algorithm based on optical remote sensing image[D]. Chengdu: Southwest Jiaotong University, 2014.

[3] 韩现伟. 大幅面可见光遥感图像典型目标识别关键技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2013.

    韩现伟. 大幅面可见光遥感图像典型目标识别关键技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2013.

    韩现伟. 大幅面可见光遥感图像典型目标识别关键技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2013.

    Han XW. The research on key technologies of large range visible light remote sensing image typical object recognition[D]. Harbin: Harbin Institute of Technology, 2013.

    Han XW. The research on key technologies of large range visible light remote sensing image typical object recognition[D]. Harbin: Harbin Institute of Technology, 2013.

    Han XW. The research on key technologies of large range visible light remote sensing image typical object recognition[D]. Harbin: Harbin Institute of Technology, 2013.

[4] 姬晓飞, 秦宁丽. 基于光学遥感图像的目标检测与分类识别方法[J]. 沈阳航空航天大学学报, 2015, 32(1): 23-31.

    姬晓飞, 秦宁丽. 基于光学遥感图像的目标检测与分类识别方法[J]. 沈阳航空航天大学学报, 2015, 32(1): 23-31.

    姬晓飞, 秦宁丽. 基于光学遥感图像的目标检测与分类识别方法[J]. 沈阳航空航天大学学报, 2015, 32(1): 23-31.

    Ji X F, Qin N L. Target detection and classification method based on optical remote sensing image[J]. Journal of Shenyang Institute of Aeronautical Engineering, 2015, 32(1): 23-31.

    Ji X F, Qin N L. Target detection and classification method based on optical remote sensing image[J]. Journal of Shenyang Institute of Aeronautical Engineering, 2015, 32(1): 23-31.

    Ji X F, Qin N L. Target detection and classification method based on optical remote sensing image[J]. Journal of Shenyang Institute of Aeronautical Engineering, 2015, 32(1): 23-31.

[5] 刘扬, 付征叶, 郑逢斌. 高分辨率遥感影像目标分类与识别研究进展[J]. 地球信息科学学报, 2015, 17(9): 1080-1091.

    刘扬, 付征叶, 郑逢斌. 高分辨率遥感影像目标分类与识别研究进展[J]. 地球信息科学学报, 2015, 17(9): 1080-1091.

    刘扬, 付征叶, 郑逢斌. 高分辨率遥感影像目标分类与识别研究进展[J]. 地球信息科学学报, 2015, 17(9): 1080-1091.

    Liu Y, Fu Z Y, Zheng F B. Research progress of target classification and recognition in high resolution remote sensing images[J]. Journal of Earth Information Science, 2015, 17(9): 1080-1091.

    Liu Y, Fu Z Y, Zheng F B. Research progress of target classification and recognition in high resolution remote sensing images[J]. Journal of Earth Information Science, 2015, 17(9): 1080-1091.

    Liu Y, Fu Z Y, Zheng F B. Research progress of target classification and recognition in high resolution remote sensing images[J]. Journal of Earth Information Science, 2015, 17(9): 1080-1091.

[6] 张义德, 胡长雨, 胡春育. 基于卷积神经网络的遥感图像飞机检测[J]. 光电子技术, 2017( 1): 66- 71.

    张义德, 胡长雨, 胡春育. 基于卷积神经网络的遥感图像飞机检测[J]. 光电子技术, 2017( 1): 66- 71.

    张义德, 胡长雨, 胡春育. 基于卷积神经网络的遥感图像飞机检测[J]. 光电子技术, 2017( 1): 66- 71.

    Zhang YD, Hu CY, Hu CY. Aircraft detection of remote sensing images based on convolutional neural network[J]. Optoelectronic Technology, 2017( 1): 66- 71.

    Zhang YD, Hu CY, Hu CY. Aircraft detection of remote sensing images based on convolutional neural network[J]. Optoelectronic Technology, 2017( 1): 66- 71.

    Zhang YD, Hu CY, Hu CY. Aircraft detection of remote sensing images based on convolutional neural network[J]. Optoelectronic Technology, 2017( 1): 66- 71.

[7] 惠国保. 基于深层神经网络的军事目标图像分类技术[J]. 现代导航, 2016( 6): 430- 436.

    惠国保. 基于深层神经网络的军事目标图像分类技术[J]. 现代导航, 2016( 6): 430- 436.

    惠国保. 基于深层神经网络的军事目标图像分类技术[J]. 现代导航, 2016( 6): 430- 436.

    Hui GB. Military target image classification technique based on deep layer neural network[J]. Modern Navigation, 2016( 6): 430- 436.

    Hui GB. Military target image classification technique based on deep layer neural network[J]. Modern Navigation, 2016( 6): 430- 436.

    Hui GB. Military target image classification technique based on deep layer neural network[J]. Modern Navigation, 2016( 6): 430- 436.

[8] 龚怡宏. 人工智能是否终将超越人类智能——基于机器学习与人脑认知基本原理的探讨[J]. 人民论坛·学术前沿, 2016( 7): 12- 21.

    龚怡宏. 人工智能是否终将超越人类智能——基于机器学习与人脑认知基本原理的探讨[J]. 人民论坛·学术前沿, 2016( 7): 12- 21.

    龚怡宏. 人工智能是否终将超越人类智能——基于机器学习与人脑认知基本原理的探讨[J]. 人民论坛·学术前沿, 2016( 7): 12- 21.

    Gong YH. Whether artificial intelligence will eventually surpass human intelligence——based on the basic principles of machine learning and human brain cognition[J]. People's Forum: Academic Front, 2016( 7): 12- 21.

    Gong YH. Whether artificial intelligence will eventually surpass human intelligence——based on the basic principles of machine learning and human brain cognition[J]. People's Forum: Academic Front, 2016( 7): 12- 21.

    Gong YH. Whether artificial intelligence will eventually surpass human intelligence——based on the basic principles of machine learning and human brain cognition[J]. People's Forum: Academic Front, 2016( 7): 12- 21.

[9] 乔石. 基于人脑学习与记忆的多源信息融合算法研究[D]. 天津: 河北工业大学, 2015.

    乔石. 基于人脑学习与记忆的多源信息融合算法研究[D]. 天津: 河北工业大学, 2015.

    乔石. 基于人脑学习与记忆的多源信息融合算法研究[D]. 天津: 河北工业大学, 2015.

    QiaoS. The research on multi-source information fusion algorithm based on human brain learning and memory[D]. Tianjin: Hebei University of Technology, 2015.

    QiaoS. The research on multi-source information fusion algorithm based on human brain learning and memory[D]. Tianjin: Hebei University of Technology, 2015.

    QiaoS. The research on multi-source information fusion algorithm based on human brain learning and memory[D]. Tianjin: Hebei University of Technology, 2015.

[10] 高常鑫, 桑农. 基于深度学习的高分辨率遥感影像目标检测[J]. 测绘通报, 2014( S1): 108- 111.

    高常鑫, 桑农. 基于深度学习的高分辨率遥感影像目标检测[J]. 测绘通报, 2014( S1): 108- 111.

    高常鑫, 桑农. 基于深度学习的高分辨率遥感影像目标检测[J]. 测绘通报, 2014( S1): 108- 111.

    Gao CX, SangN. High resolution remote sensing image target detection based on deep learning[J]. Survey and Mapping:2014( S1): 108- 111.

    Gao CX, SangN. High resolution remote sensing image target detection based on deep learning[J]. Survey and Mapping:2014( S1): 108- 111.

    Gao CX, SangN. High resolution remote sensing image target detection based on deep learning[J]. Survey and Mapping:2014( S1): 108- 111.

[11] LuoP, TianY, WangX, et al. Switchable deep network for pedestrian detection[C]. Computer Vision & Pattern Recognition, 2014.

    LuoP, TianY, WangX, et al. Switchable deep network for pedestrian detection[C]. Computer Vision & Pattern Recognition, 2014.

    LuoP, TianY, WangX, et al. Switchable deep network for pedestrian detection[C]. Computer Vision & Pattern Recognition, 2014.

[12] LiH, ZhaoR, Wang X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification[EB/OL]. ( 2014-12-16)[2017-01-15]. https:∥arxiv.org/abs/1412. 4526.

    LiH, ZhaoR, Wang X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification[EB/OL]. ( 2014-12-16)[2017-01-15]. https:∥arxiv.org/abs/1412. 4526.

    LiH, ZhaoR, Wang X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification[EB/OL]. ( 2014-12-16)[2017-01-15]. https:∥arxiv.org/abs/1412. 4526.

[13] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. ( 2015-04-10)[2017-01-15]. https:∥arxiv.org/abs/1409.1556v6.

    SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. ( 2015-04-10)[2017-01-15]. https:∥arxiv.org/abs/1409.1556v6.

    SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. ( 2015-04-10)[2017-01-15]. https:∥arxiv.org/abs/1409.1556v6.

[14] LiuW, AnguelovD, ErhanD, et al. SSD: single shot multibox detector[C]. European Conference on Computer Vision, 2016: 21- 37.

    LiuW, AnguelovD, ErhanD, et al. SSD: single shot multibox detector[C]. European Conference on Computer Vision, 2016: 21- 37.

    LiuW, AnguelovD, ErhanD, et al. SSD: single shot multibox detector[C]. European Conference on Computer Vision, 2016: 21- 37.

[15] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 39(4): 640-651.

    Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 39(4): 640-651.

    Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 39(4): 640-651.

[16] Chen LC, PapandreouG, KokkinosI, et al. Semantic image segmentation with deep convolutional nets, atrousconvolution, fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2014, PP( 99): 1.

    Chen LC, PapandreouG, KokkinosI, et al. Semantic image segmentation with deep convolutional nets, atrousconvolution, fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2014, PP( 99): 1.

    Chen LC, PapandreouG, KokkinosI, et al. Semantic image segmentation with deep convolutional nets, atrousconvolution, fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2014, PP( 99): 1.

[17] Everingham M, van Gool L, Williams C K, et al. . The pascal visual object classes (voc) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.

    Everingham M, van Gool L, Williams C K, et al. . The pascal visual object classes (voc) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.

    Everingham M, van Gool L, Williams C K, et al. . The pascal visual object classes (voc) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.

[18] RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.

    RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.

    RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.

[19] 袁渊. 面向安防监控视频树叶遮挡检测研究[D]. 武汉: 武汉科技大学, 2015.

    袁渊. 面向安防监控视频树叶遮挡检测研究[D]. 武汉: 武汉科技大学, 2015.

    袁渊. 面向安防监控视频树叶遮挡检测研究[D]. 武汉: 武汉科技大学, 2015.

    YuanY. The research for security monitoring video leaves shade detection[D]. Wuhan: Wuhan University of Science and Technology, 2015.

    YuanY. The research for security monitoring video leaves shade detection[D]. Wuhan: Wuhan University of Science and Technology, 2015.

    YuanY. The research for security monitoring video leaves shade detection[D]. Wuhan: Wuhan University of Science and Technology, 2015.

[20] Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.

    Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.

    Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.

[21] LiH, ZhaoR, Wang X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification[EB/OL]. ( 2014-12-16)[2017-01-15]. https:∥arxiv.org/abs/1412. 4526.

    LiH, ZhaoR, Wang X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification[EB/OL]. ( 2014-12-16)[2017-01-15]. https:∥arxiv.org/abs/1412. 4526.

    LiH, ZhaoR, Wang X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification[EB/OL]. ( 2014-12-16)[2017-01-15]. https:∥arxiv.org/abs/1412. 4526.

[22] 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.

    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.

    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.

    Liu D W, Han L, Han X Y. The research on classification of high resolution remote sensing images based on depth learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

    Liu D W, Han L, Han X Y. The research on classification of high resolution remote sensing images based on depth learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

    Liu D W, Han L, Han X Y. The research on classification of high resolution remote sensing images based on depth learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

[23] 韦皓瀚, 曹国, 金挺, 等. 改进星型级联可形变部件模型的行人检测[J]. 中国图象图形学报, 2017, 22(2): 170-178.

    韦皓瀚, 曹国, 金挺, 等. 改进星型级联可形变部件模型的行人检测[J]. 中国图象图形学报, 2017, 22(2): 170-178.

    韦皓瀚, 曹国, 金挺, 等. 改进星型级联可形变部件模型的行人检测[J]. 中国图象图形学报, 2017, 22(2): 170-178.

    Wei H H, Cao G, Jin T, et al. Pedestrian detection based on improved star cascaded deformable component model[J]. Journal of Image and Graphics, 2017, 22(2): 170-178.

    Wei H H, Cao G, Jin T, et al. Pedestrian detection based on improved star cascaded deformable component model[J]. Journal of Image and Graphics, 2017, 22(2): 170-178.

    Wei H H, Cao G, Jin T, et al. Pedestrian detection based on improved star cascaded deformable component model[J]. Journal of Image and Graphics, 2017, 22(2): 170-178.

侯宇青阳, 全吉成, 魏湧明. 基于认知模型的遥感图像有效飞机检测系统[J]. 光学学报, 2018, 38(1): 0111005. Yuqingyang Hou, Jicheng Quan, Yongming Wei. Valid Aircraft Detection System for Remote Sensing Images Based on Cognitive Models[J]. Acta Optica Sinica, 2018, 38(1): 0111005.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!