Elastic registration for airborne multispectral line scanners

Abstract The multispectral line scanner is one of the most popular payloads for aerial remote sensing (RS) applications. Scanners with large field of view (FOV) improve efficiency in Earth observation. Small-volume instruments with a short focal length and a large FOV, however, may bring a problem: different nonlinear warping and local transformation exist between bands. Alignment accuracy of bands is a criterion impacting product quality in RS. A band-to-band elastic image registration method is proposed for solving the problem. Rather than ignoring the intensity variation and carrying out an intensity-based registration between bands straightforwardly, we construct feature images and use them to conduct an intensity-based elastic image registration. In this method, the idea of the inverse compositional algorithm is employed and expanded when dealing with local warping, and a smoothness constraint is also added in this procedure. Experimental results show that the proposed band-to-band registration method works well both visually and quantitatively. The outstanding performance of the method also encourages potential applications for other new types of airborne multispectral imagers.

[1]  J. Barker,et al.  Landsat Thematic Mapper band-to-band registration , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[2]  Mark R. Pickering,et al.  Efficient multi-image registration with illumination and lens distortion correction , 2005, IEEE International Conference on Image Processing 2005.

[3]  Shaun S. Gleason,et al.  Subpixel measurement of image features based on paraboloid surface fit , 1991, Other Conferences.

[4]  Andrew R. Harvey,et al.  Spectral Imaging: Instrumentation, Applications, and Analysis , 2000 .

[5]  Alistair Gorman,et al.  Generalization of the Lyot filter and its application to snapshot spectral imaging. , 2010, Optics express.

[6]  Andrew R. Harvey,et al.  Spectral imaging in a snapshot , 2000, SPIE BiOS.

[7]  Z. Yi,et al.  Multi-spectral remote image registration based on SIFT , 2008 .

[8]  Nathan S. Netanyahu,et al.  Research issues in image registration for remote sensing , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Hany Farid,et al.  Elastic registration in the presence of intensity variations , 2003, IEEE Transactions on Medical Imaging.

[10]  Luís Corte-Real,et al.  CHAIR: automatic image registration based on correlation and Hough transform , 2012 .

[11]  Hany Farid,et al.  Elastic Registration with Partial Data , 2003, WBIR.

[12]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[13]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[14]  Myungjin Choi,et al.  Fast multi-spectral image registration based on a statistical learning technique , 2010, Optical Engineering + Applications.

[15]  Robert E. Wolfe,et al.  MODIS band-to-band registration , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[16]  Marios S. Pattichis,et al.  Robust Multispectral Image Registration Using Mutual-Information Models , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[17]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .