Multi-source remote sensing data fusion: status and trends

TLDR

Rapid advances in remote‑sensing technologies, such as VHR optical sensors, SAR, and LiDAR, have spurred the development of multi‑source data fusion methods across fields from earth observation to medical imaging, yet challenges persist due to differing spatial and temporal resolutions. This review surveys current multi‑source remote‑sensing fusion techniques and explores future trends and challenges through a hierarchical classification of pixel, feature, and decision levels. The focus is on optical panchromatic and multispectral data fusion methods. Pixel‑level fusion is largely confined to optical data, while feature‑ and decision‑level fusion increasingly integrates SAR, optical, LiDAR, and other modalities, signaling a trend toward broader multi‑source applications.

Abstract

With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fusion methods have mainly focused on optical data fusion; high-level fusion includes feature level and decision level fusion of multi-source data, such as synthetic aperture radar, optical images, LiDAR and other types of data. Finally, this article summarises several trends tending to broaden the application of multi-source data fusion.