Spatial enhancement, usually denoted as Pan-sharpening, consists in increasing the spatial resolution of a multispectral (MS) image by means of a panchromatic (Pan) observation of the same scene, acquired with a higher spatial resolution, by preserving or enhancing (Thomas et al., 2008) the radiometric quality of the original MS image. Many techniques have been proposed so far for Pan-sharpening. Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported. State-of-the-art algorithms add the spatial details extracted from the Pan image into the MS data set by considering different injection strategies. The capability of efficiently modeling the relationships between MS and Pan images is crucial for the quality of fusion results and particularly for a correct recovery of local features. Context-adaptive (CA) injection models have been proposed in the MRA and more recently in the CS frameworks. In this chapter some of the most recent state-of-the-art Pan-sharpening algorithms are reported. Their performances are discussed in terms of objective and visual quality, taking into account the specific objective of spatial accuracy that is crucial for the analysis of urban areas.

B., A., S., B., L., C., Garzelli, A., & M., S. (2011). Spatial enhancement of multispectral images on urban areas. In X. YANG (a cura di), Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment (pp. 141-154). Chichester, West Sussex, Engla : John Wiley and Sons Ltd. [10.1002/9780470979563.ch10].

Spatial enhancement of multispectral images on urban areas

GARZELLI, ANDREA;
2011

Abstract

Spatial enhancement, usually denoted as Pan-sharpening, consists in increasing the spatial resolution of a multispectral (MS) image by means of a panchromatic (Pan) observation of the same scene, acquired with a higher spatial resolution, by preserving or enhancing (Thomas et al., 2008) the radiometric quality of the original MS image. Many techniques have been proposed so far for Pan-sharpening. Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported. State-of-the-art algorithms add the spatial details extracted from the Pan image into the MS data set by considering different injection strategies. The capability of efficiently modeling the relationships between MS and Pan images is crucial for the quality of fusion results and particularly for a correct recovery of local features. Context-adaptive (CA) injection models have been proposed in the MRA and more recently in the CS frameworks. In this chapter some of the most recent state-of-the-art Pan-sharpening algorithms are reported. Their performances are discussed in terms of objective and visual quality, taking into account the specific objective of spatial accuracy that is crucial for the analysis of urban areas.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/13790
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