期刊文献+

一种基于空频域特征的多传感器图像融合方法 被引量:4

An Approach Based on the Features of Space-Frequence Domain of Image for Fusion of Edgemaps Obtained Through Multi Sensors
下载PDF
导出
摘要 提出了一种基于多分辨率小波分析和高斯 -马尔可夫随机场理论的边缘图像融合方法。在图像的多分辨率小波分析的基础上 ,基于高斯 -马尔可失随机场理论 ,在不同图像的对应区域 ,用回归分析的方法分别提取一组统计参数 ,这些参量表征了图像的局部结构特征 ,计算其相似性测度 ,最后由输入图像及其特征的相似性矩阵生成融合后的边缘图像。实验证明 ,这种融合方法具有较强的适应性和可靠性 ,即使在图像信噪比较低的情况下 ,也能取得较好的融合效果。 In this paper, an approach based on multiresolution wavelet analysis and GMRF theory for the fusion of edge images obtained through multisensors is proposed. The hierarchical multiresolution wavelet information in conjunction with the context information extracted from GMRF constitutes local spatial features of images to be fused, then the similarities of these features are computed, the fusion result is achieved via computing a fusion function with respect to the similarity along with imput edge images. The effectiveness and reliability have been tested through simulations, especially on those images with low SNR.
出处 《系统工程与电子技术》 EI CSCD 2000年第4期18-22,共5页 Systems Engineering and Electronics
关键词 空频域 计算机视觉 多传感器图像融合 Image resolution Algorithm Data analysis
  • 相关文献

参考文献2

  • 1Chen C H,Graphical Models Image Processing,1998年,59卷,5期,349页
  • 2Chui C K,An Introduction to Wavelets,1992年

同被引文献20

  • 1SMITH M I, HEATHER J P. Review of image fusion technology in 2005[C].Proceedings of SPIE, Bellingham, WA, 2005, 5782:29-45.
  • 2CHAVEP S, KWARTENG A Y. Extracting spectral contrast in Landsat thematic mapper image data using selective principal component analysis[J].Engineering and Remote Sensing, 1989,55(3):339-348.
  • 3WRIGHT W A. Fast image fusion with a Markov random field[C].Proc. Int. Conf. On Image Processing and its Applications, Stevenage, UK: 1999.
  • 4MELGANI F, SERPICO S B, VERNAZZA G. Fusion of multitemporal contextual information by neural networks for multisensor remote sensing image classification[J].Integrated Computer-Aided Engineering, 2003,10(1):81-90.
  • 5BROWN et al. Bayes nets for selective perception and data fusion[C].Proceedings of SPIE 2368, 1995.
  • 6SHKVARKO Y, JAIME-RIVAS R. Remotely sensed image fusion with dynamic neural networks[C].Proceeding of 4th Int.Kharkov Symposium "Physics and Engineering of Millimeter and SubMillimeter Waves" (MSMW 2001), New Jersey, USA: 2001.
  • 7SMITH M I, HEATHER J P. Review of image fusion technology in 2005[C].Proceedings of SPIE, Bellingham, 2005:29-45.
  • 8XYDAES C, PETROVI V. Objective image fusion performance measure[J].Electronic Letters, 2000, 36(4):308-309.
  • 9C.Pohl and J.L.van Genderen,Multisensor image fusion in remote sensing:concepts,methods and applications[J].Int.J.of Remote Sensing.1988,(19):823-854.
  • 10WRIGHT.W.A.Fast Image Fusion with a Markov Random Field[J].IEEE.1999,10(4):557-561.

引证文献4

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部