Context-adaptive Pansharpening Based on Image Segmentation
Résumé
Pansharpened images are widely used synthetic representations of the Earth surface characterized by both a high spatial resolution and a high spectral diversity. They are usually generated by extracting spatial details from a high resolution PANchromatic (PAN) image and by injecting them into a low spatial resolution MultiSpectral (MS) image. The details injection is performed through injection coefficients, whose values can be either uniform for the whole image (global methods) or spatially variant (context-adaptive approaches). In this paper, we propose a context-adaptive approach in which the injection coefficients are estimated over image segments achieved via a binary partition tree segmentation algorithm. The approach is applied to two credited pansharpening algorithms based on the Gram-Schmidt orthogonalization procedure and the generalized Laplacian pyramid technique. The performance assessment is performed using two different datasets acquired by the Quick-Bird and the WorldView-3 satellites. The validation procedure, both at full and at reduced resolution, shows the suitability of the proposed approach, which reaches a good trade-off between accuracy and computational burden.
Origine : Fichiers produits par l'(les) auteur(s)
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