Context-adaptive Pansharpening based on binary partition tree segmentation

Abstract : Pansharpening is a successful application of data fusion to remotely sensed data. It aims at obtaining a detailed representation of an Earth's zone both in terms of spatial and spectral resolution. This is done through the fusion of a panchromatic and a multispectral image (having complementary spatial and spectral resolutions) that are acquired simultaneously by several optical satellites. The result of the fusion is commonly achieved by introducing the spatial details, modulated opportunely by gains, in the multispectral one. The injection gains can be estimated globally over the image, or locally, thus obtaining spatially variant values. The latter approach has been proven to achieve better results and it is based on windowing the analyzed image in squared blocks. In this paper we propose a more elaborated concept of locality, as it is based on an opportune segmentation of the target scene. In greater details, we propose to estimate the local injection gains on regions composed of pixel with similar spectral characteristic, as defined by a segmentation. Such local approach is compared to the global one and to the conventional local estimation based on overlapping and non-overlapping blocks. The performances have been assessed by using three real datasets, the first acquired by WorldView-2 and the other two by Pléiades. The analysis evidences the appreciable improvements of the performances with respect to classical schemes.
Type de document :
Communication dans un congrès
21st IEEE International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. pp.3924-3928, 〈10.1109/ICIP.2014.7025797〉
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http://hal.univ-grenoble-alpes.fr/hal-01128434
Contributeur : Vincent Couturier-Doux <>
Soumis le : lundi 9 mars 2015 - 16:56:06
Dernière modification le : samedi 28 juillet 2018 - 01:14:56

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Mauro Dalla Mura, G Vivone, R. Restaino, Jocelyn Chanussot. Context-adaptive Pansharpening based on binary partition tree segmentation. 21st IEEE International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. pp.3924-3928, 〈10.1109/ICIP.2014.7025797〉. 〈hal-01128434〉

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