Spatiotemporal Fusion in Remote Sensing

TL;DR

This paper discusses the theory and applications of spatiotemporal fusion in remote sensing, emphasizing its importance for improving data quality amidst diverse acquisition conditions and massive data volumes.

Contribution

It provides a comprehensive overview of spatiotemporal fusion theory, summarizes previous research, and describes basic concepts and applications in remote sensing.

Findings

Spatiotemporal fusion enhances data quality in remote sensing.

Fusion techniques integrate asynchronously acquired data.

The paper reviews existing methods and applications.

Abstract

Remote sensing images and techniques are powerful tools to investigate earth surface. Data quality is the key to enhance remote sensing applications and obtaining a clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor, and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works.