Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications

TL;DR

This paper reviews and unifies terminology and practices in deep multi-view fusion for remote sensing, highlighting common approaches, challenges, and future directions in the field.

Contribution

It provides a comprehensive taxonomy and harmonized terminology for deep multi-view fusion methods in Earth observation, aiding clarity and future research.

Findings

Summarizes diverse approaches in multi-view fusion

Proposes a unified terminology and taxonomy

Highlights gaps and future research directions

Abstract

The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of information. They might contain static or temporary views with different levels of resolution, in addition to having different types and amounts of noise due to sensor calibration or deterioration. A great variety of deep learning models have been applied to fuse the information from these multiple views, known as deep multi-view or multi-modal fusion learning. However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques. This article gathers works on multi-view fusion for Earth observation by focusing on the common practices and approaches used in the literature. We summarize and structure insights from several different publications concentrating on unifying points and ideas. In this manuscript, we provide a harmonized terminology while at the same time mentioning the various alternative terms that are used in literature. The topics covered by the works reviewed focus on supervised learning with the use of neural network models. We hope this review, with a long list of recent references, can support future research and lead to a unified advance in the area.