Spatiotemporal Image Fusion in Remote Sensing

TLDR

Spatiotemporal fusion combines sparse fine‑resolution images with dense coarse‑resolution ones, yet few studies fuse microwave data or merge microwave and optical imagery to fill cloud‑induced gaps. The paper reviews current spatiotemporal fusion techniques and calls for more flexible methods that handle diverse sensors and environmental conditions. The review finds that most methods focus on optical blending, highlights the need to model temporal changes for fine‑scale predictions, and suggests CNNs as a promising approach for fusing images with differing spectra.

Abstract

In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.