http://www.cnr.it/ontology/cnr/individuo/prodotto/ID186464

Pyramid-Based Multi-sensor Image Data Fusion with Enhancement of Textural Features (Contributo in atti di convegno)

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Pyramid-Based Multi-sensor Image Data Fusion with Enhancement of Textural Features (Contributo in atti di convegno) (literal)

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B. Aiazzi; L. Alparone; S. Baronti; V. Cappellini; R. Carlà; L. Mortelli (1997)
Pyramid-Based Multi-sensor Image Data Fusion with Enhancement of Textural Features
in ICIAP '97: 9th International Conference on Image Analysis and Processing, Firenze, Italia, 17-19 Settembre 1997
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B. Aiazzi; L. Alparone; S. Baronti; V. Cappellini; R. Carlà; L. Mortelli (literal)

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IROE \"Nello Carrara\" - CNR, Via Panciatichi, 64, 50127 Firenze, Italy Dip. Ing. Elettronica, University of Florence, Via S. Marta, 3, 50139 Firenze, Italy IROE \"Nello Carrara\" - CNR, Via Panciatichi, 64, 50127 Firenze, Italy Dip. Ing. Elettronica, University of Florence, Via S. Marta, 3, 50139 Firenze, Italy IROE \"Nello Carrara\" - CNR, Via Panciatichi, 64, 50127 Firenze, Italy Dip. Ing. Elettronica, University of Florence, Via S. Marta, 3, 50139 Firenze, Italy (literal)

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Pyramid-Based Multi-sensor Image Data Fusion with Enhancement of Textural Features (literal)

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Abstract

In this work, a multi-resolution procedure based on a generalized Laplacian pyramid (GLP) with a rational scale factor is proposed to merge image data of any resolution and represent them at any scale. The GLP-based data fusion is shown to be superior to those of a similar scheme based on the discrete wavelet transform (WT) according to a set of parameters established in the literature. The pyramid-generating filters can be easily designed for data of any resolutions, differently from the WT, whose filter-bank design is non-trivial when the ratio between the scales of the images to be merged is not a power of two. Remotely sensed images from Landsat TM and from Panchromatic SPOT are fused together. Textured regions are enhanced without losing their spectral signatures, thereby expediting automatic analyses for contextual interpretation of the environment. (literal)

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