期刊文献+

基于邻域重构模型的高分辨SAR图像精确配准(英文) 被引量:1

Accurate Registration of High Resolution SAR Images Based on Neighborhood Reconstruction Model
下载PDF
导出
摘要 合成孔径雷达(SAR)的成像过程使其高分辨图像的几何形变呈现局部性。针对高分辨SAR图像精确配准问题,本文提出一种基于邻域重构模型的局部转换函数.邻域重构模型首先采用重构系数刻画参考图像中每个像素点的几何位置;接着给出了一种重构控制点的选择标准使每个像素的配准误差达到最小;最后根据重构系数及控制点坐标对输入图像进行再抽样以实现配准。与经典分片线性映射相比,该模型从理论上给出了一种区域剖分准则:对于每个像素选取能使配准误差能达到最小的控制点作为重构控制点。对模拟数据和真实SAR图像进行了试验,结果表明,该模型能有效地提高配准精度。 For the accurate registration of high resolution Synthetic Aperture Radar (SAR) images,local transformation functions are preferable to global ones. In this paper,a local mapping model,namely neighborhood reconstruction model,was presented to fit the geometric distortions in SAR image registration. First,each point in the reference image was characterized by the neighboring Control Points (CP) with reconstruction weights. Then,a criterion for neighboring CP selection was proposed to minimize registration error at each point. Finally,the associated point in the sensed image was resampled according to the geometric distortions and the reconstruction weights. The theoretical support from neighborhood reconstruction model to the classical piecewise linear approach was also presented. Experiments on both simulation data and real high resolution SAR images show that the registration accuracy is improved.
出处 《光电工程》 CAS CSCD 北大核心 2010年第1期145-150,共6页 Opto-Electronic Engineering
基金 国家自然科学基金(60375003,60972150,10926197) 航科科学基金(03I53059)资助项目
关键词 精确配准 高分辨 局部变换函数 邻域重构 合成孔径雷达(SAR)图像 accurate registration high resolution local transformation function neighborhood reconstruction synthetic aperture radar (SAR) image
  • 相关文献

参考文献12

  • 1Carretero M J, Gismero M J, Asensio L A, et al. Application of the Radon transform to detect small-targets in sea clutter [J]. IET Radar Sonar and Navigatlon(S1751-8784), 2009, 3(2): 155-166.
  • 2Pathe C, Wagner W, Sabel K, et al. Using ENVISAT ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2009, 47(2): 468-480.
  • 3Krieger G, Mittermayer J, Buckreuss S, et al. Sector imaging radar for enhanced vision [J]. Aerospace Science and Technology (S1270-9638), 2003, 7(2): 147-158.
  • 4Toutin T. Review article: Geometric processing of remote sensing images: models, algorithms and methods [J]. International Journal of Remote Sensing(S0143-1161), 2004, 2500): 1893-1924.
  • 5Oliver C, Quegan S. Understanding Synthetic Aperture Radar Images [M]. Boston: Artech House, 1998: 11-63.
  • 6Goshtasby A. Piecewise linear mapping functions for image registration [J].Pattern Recognition (S0031-3203), 1986, 19(6):459-466.
  • 7Zagorchev L, Goshtasby A. A comparative study of transformation functions for nonrigid image registration [J]. IEEE Transactions on Image Processing (S1057-7149), 2006, 15(3): 529-538.
  • 8Arevalo V, Gonzalez J. An experimental evaluation of non-rigid registration techniques on Quickbird satellite imagery [J]. International Journal of Remote Sensing (S0143-1161), 2008, 29(2): 513-527.
  • 9Goshtasby A, Staib L, Studholme C, etal Nonrigid image registration: guest editors' introduction [J]. Computer Vision and Image Understanding (S1077-3142), 2003, 89(2/3): 109-113.
  • 10Siddiqui A M, Saleem M. Registration of local deformations in images [C]//The Second International Conference on Eleetrical Engineering, Lahore, Pakistan, Mar25-26, 2008: 269-274.

同被引文献12

  • 1KURUOGLU E E, ZERUBIA J. Modeling SAR Images with a Generalization of the Rayleigh Distribution [J]. IEEE Transactions on Image Processing (S1057-7149), 2004, 13(4): 527-533.
  • 2ACHIM A, KURUOGLU E E, ZERUBIA J. SAR Image Filtering Based on the Heavy-tailed Rayleigh Model [J]. IEEE Transactions on Image Processing (S1057-7149), 2006, 15(9): 2686-2693.
  • 3XIE H, PIERCE L E, ULABY F T. Statistical Properties of Logarithmically Transformed Speckle [J]. IEEE Transactions on Geoseienee and Remote Sensing (S0196-2892), 2002, 40(3): 721-727.
  • 4HERVET E, FJORTOFT R, MARTHON P, et al. Comparison of Wavelet-based and Statistical Speckle Filters [C]// European Symposium on Remote Sensing, SAR Image Analysis, Modelling, and Techniques III, Barcelona, Spain, Sept 21-25, 1998: 43-54.
  • 5NIKIAS C L, SHAO M. Signal processing with alpha-stable distributions and applications [M]. New York: Wiley, 1995.
  • 6SUN Zeng-guo, HAN Chong-zhao. Parameter Estimation of Positive Alpha-stable Distribution Based on Negative-order Moments [C]//IEEE International Conference on Acoustics, Speech, and Signal Processing, Honolulu, Hawaii, USA, Apr 16-20, 2007: 1111409-1111412.
  • 7ACHIM A, TSAKALIDES P, BEZERIANOS A. SAR Image Denoising via Bayesian Wavelet Shrinkage Based on Heavy-tailed Modeling [J]. IEEE Transactions on Geoseienee and Remote Sensing (S0196-2892), 2003, 41(8): 1773'1784.
  • 8KAPLAN L M. Analysis of Multiplicative Speckle Models for Template-based SAR ATR [J]. IEEE Transactions on Aerospace and Electronic Systems (S0018-9251), 2001, 37(4): 1424-1432.
  • 9ULABY F T, DOBSON M C. Handbook of radar scattering statistics for terrain [M], Norwood: Artech House, 1989.
  • 10LEE J S. Speckle Suppression and Analysis for Synthetic Aperture Radar Images [J]. Optical Engineering (S0091-3286), 1986, 25(5): 636-643.

二级引证文献1

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