Image registration is a vital step in the processing of multispectral remotesensing imagery. This paper presents a robust
multispectral remotesensing image registration algorithm based on maximally stable extremal regions (MSERs). Firstly,
MSERs are detected independently in the reference image and the sensed image. Secondly, the SIFT descriptor is
adopted to capture texture information in the detected regions, while an affine invariant shape descriptor for MSER is
constructed to ensure that features can be reliably matched regardless of the appearance change. Both the SIFT
descriptors and the shape descriptors are matched using the Euclidean distance measurement. Matching results are then
combined and the optimal corresponding points are chosen to estimate the transformation parameters. Finally, random
sample consensus (RANSAC) algorithm is applied for geometry estimation. Experimental results on various image pairs
demonstrate that the proposed MSER based algorithm is very effective for multispectral remotesensing image
registration.
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