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基于Fourier-Mellin变换的气象卫星光谱图像配准 被引量:11

The Meteorological Satellite Spectral Image Registration Based on Fourier-Mellin Transform
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摘要 气象卫星光谱图像是气象科学和环境遥感科学研究的重要工具,而图像配准是气象卫星图像数据应用的前提。文章针对气象卫星光谱图像的配准问题,提出了一种基于Fourier-Mellin变换的自动配准方法。首先利用全球海岸线矢量图数据构造地标模板,地标模板是气象卫星光谱图像配准的参考图像;其次,根据云通道数据选择无云区域红外子图像,并利用Sobel算子对红外光谱图像提取边缘特征;最后利用Fourier-Mellin变换确定地标模板图像和红外边缘图像之间的仿射变换参数,从而实现红外光谱图像的配准。该方法本质是基于曲线匹配的思想,无需特征点提取,大大简化了配准流程。利用FY-2D气象卫星上获取的红外通道数据进行了实验,结果表明:该方法鲁棒性好,运算速度快,配准精度较高。 The meteorological satellite spectral image is an effective tool for researches on meteorological science and environmental remote sensing science.Image registration is the basis for the application of the meteorological satellite spectral image data.In order to realize the registration of the satellite image and the template image,a new registration method based on the Fourier-Mellin transform is presented in this paper.Firstly,we use the global coastline vector map data to build a landmark template,which is a reference for the meteorological satellite spectral image registration.Secondly,we choose infrared sub-image of no cloud according to the cloud channel data,and extract the edges of the infrared image by Sobel operator.Finally,the affine transform model parameters between the landmark template and the satellite image are determined by the Fourier-Mellin transform,and thus the registration is realized.The proposed method is based on the curve matching in essence.It needs no feature point extraction,and can greatly simplify the process of registration.The experimental results using the infrared spectral data of the FY-2D meteorological satellite show that the method is robust and can reach a high speed and high accuracy.
出处 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2013年第3期855-858,共4页 Spectroscopy and Spectral Analysis
基金 国家自然科学基金项目(61101207 61271435)资助
关键词 光谱图像 图像配准 FOURIER-MELLIN变换 Spectral image Image registration Fourier-Mellin transforms
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  • 1王海南,郝重阳,雷方元,张先勇.非刚性医学图像配准研究综述[J].计算机工程与应用,2005,41(11):180-184. 被引量:24
  • 2刘宝生,闫莉萍,周东华.几种经典相似性度量的比较研究[J].计算机应用研究,2006,23(11):1-3. 被引量:44
  • 3李晓明,郑链,胡占义.基于SIFT特征的遥感影像自动配准[J].遥感学报,2006,10(6):885-892. 被引量:153
  • 4张莉,汪大明.Forstner算子及其改进[J].北京工业职业技术学院学报,2007,6(3):17-18. 被引量:9
  • 5Berthilsson R.Affine correlation[C]//Proceedings of the 14th International Conference on Pattern Recognition ICPR'98,Brisbane,Australia,1998,2:1458-1461.
  • 6Simper A.Correcting general band-to-band misregistrations[C]//Proceedings of the IEEE International Conference on Image Processing ICIP'96,Lausanne,Switzerland,1996,2:597-600.
  • 7Xiaowei Han,Lei Yan,Hongying Zhao.An approch of fast image mosaic based on binary region segmentation[J].SPIE,2007,6279:57-62.
  • 8B S Reddy,B N Chatterji.An FFT-based technique for transaction,rotation and scale invariant image registration[J].IEEE Transaction on Image Processing,1996,5(8):1266-1271.
  • 9Jing Zhang,Zongying Ou,Weiqing Chen.A panoramic image mosaic algorithm based on wavelet decomposition and equidistant matching[J].SPIE,2004,5444:145-148.
  • 10Miroslav T,Mark H.Fast Corner Detection[J].Image and Vision Computing,1998,16(1):75-87.

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  • 1许秀贞,李自田,薛利军.CCD噪声分析及处理技术[J].红外与激光工程,2004,33(4):343-346. 被引量:105
  • 2刘卫光,崔江涛,周利华.插值和相位相关的图像亚像素配准方法[J].计算机辅助设计与图形学学报,2005,17(6):1273-1277. 被引量:29
  • 3李晓明,赵训坡,郑链,胡占义.基于Fourier-Mellin变换的图像配准方法及应用拓展[J].计算机学报,2006,29(3):466-472. 被引量:50
  • 4高军,李学伟,张建,卢秉恒.基于模板匹配的图像配准算法[J].西安交通大学学报,2007,41(3):307-311. 被引量:36
  • 5Li H,Manjunath B S,Mitra S K. A contour based approach to multisensor image registration[J]. IEEE Trans. on Image Processing, 1995,4(3) : 320-334.
  • 6Liu H Z,Guo B L,Feng Z Z. Pesudo-log-pola Fourier transform for image registration[J]. IEEE Signal Processing Letters,2006,13(1) :17-20.
  • 7CHU P L.Efficient detection of small moving objects[R].Lexington,Massachusetts:Lincoln Laboratory,Massachusetts Institute of Technology,1989.
  • 8POHLIG S C.Maximum likelihood detection of electro-optic moving targets[R].Lexington,Massachusetts:Lincoln Laboratory,Massachusetts Institute of Technology,1992.
  • 9LOWED G.Distinctive image features from scaleinvariant keypoints[J].International Journal of Computer Vision,2004,60(2) :91-110.
  • 10LOWED G.Local feature view clustering for 3D object recognition[C].Proceedings of the 2001IEEE Computer Society Conference on Computer Wsion and Pattern Recognition,2001,1:682-688.

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