A context-driven pansharpening method using superpixel based texture analysis

Abstract

ABSTRACT In this paper, we propose a context-driven injection scheme for pansharpening, in which the injection coefficients are computed over superpixel segments obtained by means of a modified Simple Linear Iterative Clustering (t-SLIC) technique applied on the texture descriptors of the PAN image. By using the t-SLIC algorithm, various homogeneous-connected components can be generated according to their spectral properties. The proposed pansharpening method relies on a multiresolution framework by employing the Generalized Laplacian Pyramid (GLP) tailored to the Modulation Transfer Function (MTF) of the MS sensors for extracting the high frequency details. First, the injection gains are locally computed as regression coefficients between the upsampled MS and low-resolution PAN regions at a reduced scale. Then, they are multiplied by a global weighting factor computed per spectral band and defined as the ratio of variance between expanded MS bands and PAN image. Finally, the spatial details are modulated by means of the estimated global-local injection coefficients at superpixel level to produce the high-resolution MS image. The validation is assessed with two datasets acquired by IKONOS and WorldView-3 satellites. The experimental results show that the proposed method achieves a favourable performance both visually and quantitatively compared to the state of-the-art pansharpening algorithms.