Low Rank Tensor Completion Pansharpening Based on Haze Correction

Abstract

Pansharpening refers to the fusion between a multispectral (MS) image with abundant spectral information and a panchromatic (PAN) image with high spatial resolution to obtain a high spatial resolution multispectral (HRMS) image. The traditional pansharpening methods often ignore the effect of path-radiation caused by scattering from different atmospheric components, and the few methods that introduce haze correction only calibrate each band of the MS image individually, without exploring the intrinsic correlation among different bands. To address this problem, low rank tensor completion pansharpening based on haze correction (LRTCP) is proposed. The haze-line prior is first introduced into the joint haze correction of MS and PAN images, and obtain the pre-modulated images with the help of the improved high-pass modulation (HPM) injection scheme. We then use tensor completion to simulate the degradation problem by applying low-tubal-rank tensor complementation to the process of reconstructing HRMS images, thus constructing a low rank tensor completion pansharpening model based on haze correction. Finally, the alternating direction multiplier (ADMM) is employed to find the solution of the proposed approach, producing the final fusion result. Comprehensive qualitative and quantitative assessment of reduced- and full-resolution datasets from different satellites shows that the proposed method outperforms the state-of-the-art methods.