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.

中文翻译：

---

不同测绘带宽度的全色、多光谱、高光谱遥感影像集成融合


紫苑-1 02D卫星同时提供低空间分辨率（LR）窄幅宽高光谱（HS）图像、中等空间分辨率（MR）宽幅宽多光谱（MS）图像和高光谱图像。空间分辨率 (HR) 全色 (PAN) 图像，具有与 MR MS 相同的宽测绘带宽度。如何全面整合二者的互补优势，获得宽幅宽、高保真度的HR HS图像，是有趣但具有挑战性的。在这封信中，我们提出了一种针对不同测绘带宽度的 HR PAN、MR MS 和 LR HS 图像的集成融合方法，以生成最佳的宽测绘带宽度 HR HS 图像。所提出的方法基于编码器-解码器学习框架。在所提出的融合框架中，考虑到多分支编码器（MBE）结构之间的空间和光谱分辨率的差异，设计了一种具有增强HS编码器模块（EHM）和多级空间频谱聚合块（MSSAB）的新型多分支编码器（MBE）结构。多传感器图像。对合成数据集和真实数据集的实验从定性和定量方面证明了该方法的竞争性能。