Pansharpening With Joint Local Low Rank Decomposition and Hierarchical Geometric Filtering

Yuteng Gao,Chengtian Song,Shuyuan Yang,Min Wang,Chen Yang

doi:10.1109/access.2019.2940482

Yuteng Gao, Chengtian Song + Show 3 more

Open Access

https://doi.org/10.1109/access.2019.2940482

Copy DOI

Abstract

Extracting matched details of the PANchromatic (PAN) image and injecting them into the MultiSpectral (MS) images, is very crucial in pansharpening. In this paper, a new pansharpening method based on Joint Local Low Rank Decomposition (JLLRD) and Hierarchical Geometric Filtering (HGF) is proposed. First, a cascaded geometric filtering is performed on the PAN and MS images, to extract their multi-scale directional details. Then a joint local low rank decomposition is developed to deduce low-rank and sparse components for injection. Finally, an adaptive injection rule based on spectral correlation coefficient, is designed to further reduce spectral distortion of the fused images. Several experiments are taken to investigate the performance of the proposed JLLRD-HGF method, and the results show that it can extract more accurate injection details and produce less spectral and spatial distortions than its counterparts.

Highlights

Pansharpening technology aims to fuse high spatial resolution PANchromatic (PAN) image with MultiSpectral (MS) images, to obtain High spatial resolution MS (HMS) images
Many pansharpening methods have been developed, which can be categorized as three groups [1]: 1) Component Substitution (CS) [2], [3], 2) Amélioration de la Résolution Spatiale par Injection de Structures (ARSIS) [4], [5], and 3) Sparse Representation (SR) [6], [7], [30] based methods
We address these issues and propose a new pansharpening method based on Joint Local Low Rank Decomposition (JLLRD) and Hierarchical Geometric Filtering (HGF)

Summary

Introduction

Pansharpening technology aims to fuse high spatial resolution PANchromatic (PAN) image with MultiSpectral (MS) images, to obtain High spatial resolution MS (HMS) images. Exploring the multi-scale characteristic of images in the low-rank pansharpening methods can help to improve the fusion results.

Objectives

Results

Conclusion