A Spatiotemporal Fusion Method Based on Multiscale Feature Extraction and Spatial Channel Attention Mechanism

Abstract

Remote sensing satellite images with a high spatial and temporal resolution play a crucial role in Earth science applications. However, due to technology and cost constraints, it is difficult for a single satellite to achieve both a high spatial resolution and high temporal resolution. The spatiotemporal fusion method is a cost-effective solution for generating a dense temporal data resolution with a high spatial resolution. In recent years, spatiotemporal image fusion based on deep learning has received wide attention. In this article, a spatiotemporal fusion method based on multiscale feature extraction and a spatial channel attention mechanism is proposed. Firstly, the method uses a multiscale mechanism to fully utilize the structural features in the images. Then a novel attention mechanism is used to capture both spatial and channel information; finally, the rich features and spatial and channel information are used to fuse the images. Experimental results obtained from two datasets show that the proposed method outperforms existing fusion methods in both subjective and objective evaluations.