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Proceedings Paper

Dense registration of CHRIS-Proba and Ikonos images using multi-dimensional mutual information maximization
Author(s): Claude Cariou; Kacem Chehdi
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Paper Abstract

We investigate the potential of multidimensional mutual information for the registration of multi-spectral remote sensing images. We devise a gradient flow algorithm which iteratively maximizes the multidimensional mutual information with respect to a differentiable displacement map, accounting for partial derivatives of the multivariate joint distribution and the multivariate marginal of the float image with respect to each variable of the mutual information derivative. The resulting terms are shown to weight the band specific gradients of the warp image, and we propose in addition to compute them with a method based on the k-nearest neighbours. We apply our method to the registration of Ikonos and CHRIS-Proba images over the region of Baabdat, Lebanon, for purposes of cedar pines detection. A comparison between (crossed) single band and multi-band registration results obtained shows that using the multidimensional mutual information brings a significant gain in positional accuracy and is suitable for multispectral remote sensing image registration.

Paper Details

Date Published: 17 October 2013
PDF: 10 pages
Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920C (17 October 2013); doi: 10.1117/12.2028998
Show Author Affiliations
Claude Cariou, TS12M, IETR, Univ. de Rennes 1 (France)
Kacem Chehdi, TS12M, IETR, Univ. de Rennes 1 (France)

Published in SPIE Proceedings Vol. 8892:
Image and Signal Processing for Remote Sensing XIX
Lorenzo Bruzzone, Editor(s)

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