Online multi-resolution fusion of space-borne multispectral images

TL;DR

This paper introduces a recursive fusion method for multi-temporal, multi-spectral satellite images that enhances spatial resolution and revisiting frequency by leveraging spatial relationships across modalities.

Contribution

It presents a novel recursive state estimation approach for fusing multi-resolution satellite images, improving resolution and revisit rates over existing methods.

Findings

Outperforms existing fusion techniques on real Landsat and MODIS data

Enhances spatial resolution and temporal revisit rate simultaneously

Demonstrates effectiveness in filtering and smoothing contexts

Abstract

Satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena. One important feature of satellite images is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. The proposed strategy clearly outperforms competing methodologies, which is shown in the paper for real data acquired by the Landsat and MODIS instruments.