FTDN: Multispectral and Hyperspectral Image Fusion With Diverse Temporal Difference Spans

Multispectral (MS)-hyperspectral (HS) image fusion, which aims to enhance the spatial resolution of low-spatial-resolution HS images with a high-spatial-resolution MS, has provided a wide range of applications in remote sensing. However, relatively long revisit cycles of HS satellites and irresistible weather factors cause the acquisition of HS and MS images at the same time difficult. Most of the existing approaches neglect the temporal difference between MS and HS images and perform weakness in the challenging case with diverse temporal difference spans. In this article, we propose a novel image fusion strategy with embedding a stage of feature matching before interaction. On the one hand, we explore the role of spectral correlation modeling between HS and MS images, which accounts for the utilization of available spatial information from MS images. On the other hand, we design a feature aggregation module to fully exploit the nonlinear gaps and dependencies of heterogeneous data and utilize adaptive gains to realize complementary information projection and fusion. We build Dongying (DY) and Yellow River Estuary (YRE) remote-sensing datasets based on Sentinel-2 and ZiYuan (ZY)-1 02D satellites with diverse temporal difference spans. The extensive experiments demonstrate that our method is robust to the span of temporal difference and shows superior performance over the existing methods visually and quantitatively.