29 October 2014 Automatic registration of multispectral images through maximization of mutual information
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Abstract
In this paper we propose a method to get fine registration of high resolution multispectral images. The algorithm supposes that a coarse registration, based on ancillary information, has been already performed. It is known, in fact, that residual distortions remain, due to the combined effects of Earth rotation and curvature, view geometry, sensor operation, variations in platform velocity, atmospheric and terrain effects. The algorithm grounds its main idea on the information-theoretic approach to register volumetric medical images of different modalities. Registration is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized. The idea is that the join information is maximized when the two images are at their best registration. This approach works directly with image data but in principle it can be applied in any transformed domain. While the original algorithm has been thought to make registration in a limited search space (i.e. translation and orientation), in the remote sensing framework the class of transformations is extended allowing scaling, shearing or a general polynomial model. The maximization of the target function is performed using both the stochastic gradient descent algorithm and the simulated annealing, since the former is known to occasionally deadlock in local maxima. We have applied the algorithm on a SPOT-5 couple of images, achieving the registration of chips of size 256x256 pixels at time. Accuracy has been obtained comparing the results with the outcomes of a commercial software that adopts a sort of Normalized Cross-Correlation method. On 143 chips taken throughout the image, the final translation accuracy resulted well below 1 pixel and the rotation accuracy about 0.015deg.
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Pietro Guccione, Luigi Mascolo, Giuseppe Cifarelli, Cristoforo Abbattista, Mario Tragni, "Automatic registration of multispectral images through maximization of mutual information", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92440C (29 October 2014); doi: 10.1117/12.2065296; https://doi.org/10.1117/12.2065296
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