.png){kind=icon}
.png){kind=icon}
Return
A robust and efficient deep optimization network for spatiotemporal data fusion
.png){kind=icon}
PRE
AI
DOI：10.1016/j.inffus.2025.103939.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Satellites strive to strike a delicate balance between temporal and spatial resolution, thereby rendering the achievement of high resolution in both aspects challenging. Spatiotemporal fusion algorithms have emerged as a promising solution to tackle this challenge. However, with changes in spatiotemporal conditions, existing spatiotemporal fusion methods, particularly those based on deep learning, face challenges such as decreased prediction accuracy and poor reconstruction accuracy in areas of abrupt changes. This presents significant challenges for the fusion of multi-source remote sensing data to generate cloud-free remote sensing images on a daily scale. In this context, the study proposes a multiscale Attention-Guided deep optimization network for Spatiotemporal Data Fusion (AGSDF) method. The algorithm is designed to generate daily fine images using coarse image, based on historical reference fine images. Specifically, it firstly attempts to use a physical attention mechanism to mitigate the effects of climate change in time-series images. Implementing a continuous spatiotemporal fusion process across multiple scales significantly enhances the model's robustness. The performance of AGSDF was evaluated and compared to nine methods at six sites worldwide. The experimental results indicate that AGSDF achieved a top score in the assessment. Consequently, AGSDF holds high potential to produce accurate remote sensing products with high temporal and spatial resolution across extensive regions.