Dense registration of CHRIS-Proba and Ikonos images using multi-dimensional mutual information maximization

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.
Type de document :
Communication dans un congrès
Conference on Image and Signal Processing for Remote Sensing XIX, Sep 2013, Dresde, Germany. 8892, pp.UNSP 88920C, 2013, Proceedings of SPIE. 〈10.1117/12.2028998〉
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https://hal.archives-ouvertes.fr/hal-00959490
Contributeur : Yolande Sambin <>
Soumis le : vendredi 14 mars 2014 - 15:37:40
Dernière modification le : mercredi 16 mai 2018 - 11:23:47

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Claude Cariou, Kacem Chehdi. Dense registration of CHRIS-Proba and Ikonos images using multi-dimensional mutual information maximization. Conference on Image and Signal Processing for Remote Sensing XIX, Sep 2013, Dresde, Germany. 8892, pp.UNSP 88920C, 2013, Proceedings of SPIE. 〈10.1117/12.2028998〉. 〈hal-00959490〉

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