A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images

Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported when merging multispectral (MS) and panchromatic (Pan) images (pansharpening), acquired with different spatial and spectral resolutions. State-of-the-art algorithms add the spatial details extracted from the Pan into the MS data set by considering different injection strategies. The capability of efficiently modeling the relationships between MS and Pan is crucial for the quality of fusion results and particularly for a correct recovery of local features with a consequent reduction of spectral distortions. Although context-adaptive (CA) injection models have been proposed in the MRA framework, their adoption in CS schemes has been scarcely investigated so far. In this letter, CA strategies are compared with global models by considering a general protocol in which both MRA- and CS-based schemes can be described. Qualitative and quantitative results are reported for three high-resolution data sets from two different sensors, namely, IKONOS and simulated Pléiades. The score gains of well-known and novel quality figures show that CA models are more efficient than global ones.