An efficient registration and fusion algorithm for large misalignment remote sensing images

Lingling Li; Cuihua Li; Xiaoming Zeng; Bao Li

doi:10.1117/12.749414

14 November 2007 An efficient registration and fusion algorithm for large misalignment remote sensing images

Lingling Li, Cuihua Li, Xiaoming Zeng, Bao Li

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 67901X (2007) https://doi.org/10.1117/12.749414
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

In this paper, an efficient technique to perform automatic registration and fusion for large misalignment remote sensing images is proposed. It complements SIFT features with Harris-affine features, and uses the ratio of the first and second nearest neighbor distance to setup the initial correspondences, then uses the affine invariant of Mahalanobis distance to remove the mismatched feature points. From this correspondence of the points, the affine matrix between two different images can be determined. All points in the sensed image are mapped to the reference using the estimated transformation matrix and the corresponding gray levels are assigned by re-sampling the image in the sensed image. Finally, we develop Burt's match and saliency metric and use neighborhood space frequency to fuse the registrated reference and sensed remote sensing images in NSCT domain. Experiments on remote sensing images with large misalignment demonstrate the superb performance of the algorithm.

Citation Download Citation

Lingling Li, Cuihua Li, Xiaoming Zeng, and Bao Li "An efficient registration and fusion algorithm for large misalignment remote sensing images", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67901X (14 November 2007); https://doi.org/10.1117/12.749414

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KEYWORDS

Image fusion

Image registration

Remote sensing

Mahalanobis distance

Data fusion

Mendelevium

Sensors

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