LAUSR

Zi Yuan (ZY)-1 02D satellite simultaneously provides the low spatial resolution (LR) and narrow swath-width hyperspectral (HS) image, the moderate spatial resolution (MR) multispectral (MS) image with a wider swath width, and the high spatial resolution (HR) panchromatic (PAN) image with the same wide swath width to the MR MS. How to comprehensively integrate their complementary advantages to obtain the wide swath-width and high-fidelity HR HS image is interesting but challenging. In this letter, we propose an integrated fusion method for the HR PAN, MR MS, and LR HS images with different swath widths to generate the optimal wide swath-width HR HS image. The proposed method is based on the encoder–decoder learning framework. In the proposed fusion framework, a novel multibranch encoder (MBE) structure with an enhanced HS-encoder module (EHM) and the multilevel spatial–spectral aggregation block (MSSAB) is designed, by considering the difference in the spatial and spectral resolution among the multisensor images. The experiments on synthetic and real datasets from both qualitative and quantitative aspects demonstrated the competitive performance of the proposed method.