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基于PCNN的海冰SAR图像分类系统 被引量：5

A SAR sea ice image classification system based on PCNN
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摘要 简化和改进了脉冲耦合神经网络(PCNN),建立了基于时间索引图的脉冲耦合神经网络海冰 SAR 图像分类器,用于海冰 SAR 图像的分割和海冰分类。在此基础上建立了基于人工解译的半自动海冰分类判读系统。将发展的分类器用于辽东海湾冰探测,结果表明这个分类器能够区分海冰和海水,识别不同海冰类型,且具有高效率。为了选择适合辽东湾海冰分类的 PCNN 参数,分析了链接半径、链接强度和索引图等级等参数,给出了各参数合适的取值范围及调节原则。 The pulse-coupled neural network （pCNN）was simplified and improved,and a PCNN SAR sea ice image classifier based on time-index images was proposed.Then a semi-automatic sea ice classification system with a man-machine interface was built based on the artificial interpretation.The classifer proposed was used in the detection and classification of sea ice in Liaodong bay.The results show that the classifer can distinguish sea ice from open water and recognize the different ice types with high efficiency.In order to select PCNN parameters suitable for classification of sea ice in Liaodong bay,the linking radius,the linking strength and the time-index rank were analyzed,and their value ranges and regulatory principles were given.
作者 纪永刚 张杰 孟俊敏 张汉德
机构地区 国家海洋局第一海洋研究所 国家海洋局北海分局
出处 《高技术通讯》 CAS CSCD 北大核心 2008年第2期190-195,共6页 Chinese High Technology Letters
基金 国家863计划(2001AA633080)资助项目
关键词 脉冲耦合神经网络(PCNN) 合成孔径雷达(SAR) 辽东湾海冰海冰分类 pulse-coupled neural network （PCNN）, synthetic aperture radar （SAR）, Liaodong bay, sea ice, iceclassification
分类号 TN958 [电子电信—信号与信息处理] Q949.751.9 [生物学—植物学]
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参考文献13

  • 1Simon H. Remote Sensing of Sea Ice and Icebergs. New York: John Wiley and Sons, Inc., 1994. 540-569.
  • 2Makynen M, Hallikainen M. Investigation of C-Band and X- band backscattering signatures of the Baltic sea ice. International Journal of Remote Sensing ,2004, 25(11): 2061-2086.
  • 3Qiyao Y, Moloney C, Williams F. M. SAR Sea-ice texture classification using discrete wavelet transform based methods. In : Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2002. 3041-3043.
  • 4Tsatsoulis C , Soh L K. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2):780-795.
  • 5Clausi D A. Texture Segmentation on SAR Ice Inragery: [PhD dissertation]. Waterloo, Canada: University of Waterloo, System Design Engineering, 1996.
  • 6Smilh D M, Barett E C, Scott J C. Sea ice type classification from ERS-1 SAR data based on gray level and texture informarion. Polar Record, 1995, 31(177) : 135-146.
  • 7Nazmy T M. Evaluation of the PCNN standard model for image processing purposes. International Journal of Intelligent Computing and Informations Sciences, 2004, 4(2) : 101-111.
  • 8Johnson J L, Padgett M L. PCNN models and applications. IEEE Transactions on Neural Networks, 1999, 10(3) : 480-498.
  • 9Kuntimad G, Ranganath H S. Perfect image segmentation using pulse coupled neural networks. IEEE Transactions on Neural Networks, 1999, 10(3) : 591-598.
  • 10Ranganath H S, Kuntimad G. Object detection using pulse coupled neural networks. IEEE Transactions on Neural Networks. 1999, 10 (3): 615-620.

二级参考文献6

  • 1Eckhorn R, Reitboeck H J, Arndt M, et al. Feature linking via synchronization among distributed assemblies: Simulation of results from cat visual cortex [ J ]. Neural Computing, 1990, 2: 293-307.
  • 2Ranganath H S, Kuntimad G, Johnson J L. Pulse-coupled neural network for image processing[ C ]. In: Proceedings of IEEE Southeastcon, New York: IEEE Press, 1995.
  • 3Johnson J L, Padgett H J, Arddt M, et al. PCNN model and applications [ J ]. IEEE Transaction on Neural Networks, 1999, 10(3) : 480-498.
  • 4Kuntimad G, Ranganath H S. Perfect image segmentation using pulse-coupled neural networks [ J ]. IEEE Transactionon Neural Networks, 1999, 10(3) : 591-598.
  • 5Zhang J Y, Bao Z. On condition of perfect image segmentation [ C ]. In - Proceedings of the 8th International Conference on Neural Information Processing, Shanghai, China, 2001.
  • 6孔祥维,黄静,石浩.基于改进的脉冲耦合神经网络的红外目标分割方法[J].红外与毫米波学报,2001,20(5):365-369. 被引量：11

共引文献13

同被引文献42

引证文献5

二级引证文献32

高技术通讯
2008年 第2期

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