Context-Adaptive Pansharpening Based on Image Segmentation

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 image and by injecting them into a low spatial resolution multispectral 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 (CA) approaches). In this paper, we propose a CA approach in which the injection coefficients are estimated over image segments achieved through 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 data sets acquired by the QuickBird 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 tradeoff between accuracy and computational burden.