Panchromatic Sharpening of Remote Sensing Images Using a Multi-scale Approach

TL;DR

This paper introduces a multi-scale wavelet Kalman particle filter approach for remote sensing image fusion, significantly enhancing spectral and spatial quality over existing methods by addressing model assumptions and leveraging advanced filtering techniques.

Contribution

It proposes a novel wavelet Kalman particle filter method that improves spectral and spatial fidelity in remote sensing image fusion, with detailed analysis of model assumptions and multiscale implementation.

Findings

Improved fusion quality in terms of correlation coefficient, ERGAS, UIQI, and Q4.

Outperforms existing methods like IHS, PCA, wavelet Kalman filter.

Model is more consistent with natural MS and PAN images.

Abstract

An ideal fusion method preserves the Spectral information in fused image and adds spatial information to it with no spectral distortion. Recently wavelet kalman filter method is proposed which uses ARSIS concept to fuses MS and PAN images. This method is applied in a multiscale version, i.e. the variable index is scale instead of time. With the aim of fusion we present a more detailed study on this model and discuss about rationality of its assumptions such as first order markov model and Gaussian distribution of the posterior density. Finally, we propose a method using wavelet Kalman Particle filter to improve the spectral and spatial quality of the fused image. We show that our model is more consistent with natural MS and PAN images. Visual and statistical analyzes show that the proposed algorithm clearly improves the fusion quality in terms of: correlation coefficient, ERGAS, UIQI, and Q4; compared to other methods including IHS, HMP, PCA, A`trous, udWI, udWPC, Adaptive IHS, Improved Adaptive PCA and wavelet kalman filter.