期刊文献+

遥感影像配准方法的研究和应用 被引量:1

Study and Application of Methods of Remote Sensing Image Registration
下载PDF
导出
摘要 图像配准是图像处理的基本任务之一,用于将不同时间、不同传感器、不同视角及不同拍摄条件下获取的两幅或多幅图像进行(主要是几何意义上的)匹配。它是一个把两幅图像对齐到同一个坐标系下以分析其间细微变换的处理过程。实现图像配准主要有空间域方法和频率域方法两类。针对对序列遥感影像的处理,实现了这两类方法中各具代表性的算法,并在这个过程中根据实际情况做出剔除外点的提纯决策和特定倍数的区域傅里叶变换,然后进行比较。实验表明,频率域方法能取得更精确的配准结果。最后,结合实验结果对空间域算法进行了分析,得出了结论。 Image registration is a fundamental task in image processing, which geometrically matches two or more images taken at different time, from different sensors or from different viewpoints. It is a process of aligning two images into a common coordinate system to monitor and analyze the subtle changes between the two. The image registration methods are mainly divided into two types: spatial domain registration method and frequency domainregistration method. For the processing of se- ries remote sensing images, two representative algorithms of each type were implemented by making the purification to get rid of outliers and Fourier transform in certain neighborhood according to the actual situation, and were compared. The experiment results show that the frequency domain method can achieve more accurate registration. At the end of the paper, the spatial domain algorithm is analysed with respect to the experiment results, and a conclusion is offered.
出处 《现代电子技术》 2011年第4期87-90,共4页 Modern Electronics Technique
基金 国家自然科学基金资助项目(60972088) 长江学者和创新团队发展计划资助(IRT0705)
关键词 图像配准 空间域 频率域 遥感影像 image registration spatial domain frequency domain remote sensing image
  • 相关文献

参考文献10

  • 1LOWED G. Distinctive image features from scale-invariant key points[J].International Journal of Computer Vision, 2004, 60 (2): 91-110.
  • 2GUIZAR-SICAIROS M, THURMAN S T, FIENUP J R. Efficient subpixel image registration algorithm [J]. Optics Letters, 2008, 33 (2): 156- 158.
  • 3LOWED G. Object recognition from local scale-invariant features [C]// Proceedings of IEEE Seventh International Conference on Computer Vision. Corfu, Greece: ICCV, 1999, 2: 151-158.
  • 4薛年喜.Matlab在数字信号处理中的应用[D].长沙:湖南大学,2005.
  • 5FISCHLER M, BOLLES R. Random sample consensus: a paradigm for model fitting with applications to image analy- sis and automated cartography[J].ACM, Graphics and Image Processing, 1981 , 24 (6) : 1- 8.
  • 6FIENUP J R, KOWALCZYK A M. Phase retrieval for a complex-valued object by using a low-resolution image [J]. Opt. Soc. Am, 1990, A (7): 450- 458.
  • 7PARK S C, PARK M K, KANG M G. Super resolution image reconstruction: a technical overview[J].IEEE Signal Processing Magazine, 2003, 20 (3):21-3.
  • 8TORR P H S, MURRAY D W. The development and comparison of robust methods for estimating the {undamental matrix[J].International Journal of Computer Vision, 1997, 24 (3): 271-300.
  • 9SUBBARAO R, MEER P. Beyond RANSAC: user pendent robust regression [C]// Proceedings of 2006 ference on Computer Vision and Pattern Recognition. gers University, USA: CCVPR, 2006:101-110.
  • 10邵秀娟,胡炳樑,闫鹏.星空背景中目标识别算法研究[J].现代电子技术,2010,33(4):163-165. 被引量:3

二级参考文献12

共引文献2

同被引文献4

引证文献1

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