Quaternion-Based Sparse Model for Pan-Sharpening of IRS Satellite Images

Abstract

This paper considers the pan-sharpening problem of the IRS satellite images from the perspective of vector sparse representation model using quaternion matrix analysis. It selects the sparse basis in quaternion space, which uniformly transforms the color channels into an orthogonal color space. Moreover, the proposed quaternion model for pan-sharpening is more efficient than the conventional sparse model as the hyper-complex representation of color channels conserves the interrelationship among the chromatic channels. This paper also proposes a quaternion forward–backward pursuit algorithm that preserves the inherent chromatic structures in terms of spatial and spectral details during the vector reconstruction. The experimental result validates the efficacy of the proposed quaternion model and shows its potential as a powerful pan-sharpening tool for IRS data even for cloudy multispectral data.