**Hyperspectral Image SuperȬResolution with SelfȬSupervised SpectralȬSpatial Residual Network**

**Wenjing Chen 1,2, Xiangtao Zheng 1,\* and Xiaoqiang Lu <sup>1</sup>**

	- luxiaoqiang@opt.ac.cn (X.L.)

This paper proposed a self-supervised, spectral–spatial residual network (SSRN) to fuse a low-spatial-resolution (LR) hyperspectral image (HSI) with a high-spatial-resolution (HR) multispectral image (MSI) to obtain HR HSIs. In particular, SSRN does not require HR HSIs as supervised information in training. SSRN considers the fusion of HR MSIs and LR HSIs as a pixel-wise spectral mapping problem wherein the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Then the spectral mapping between LR MSIs and LR HSIs was further explored by SSRN. Finally, a self-supervised fine-tuning strategy was proposed to transfer the learned spectral mapping to generate HR HSIs. Simulated and real hyperspectral databases were utilized to verify the performance of SSRN.

remotesensingȬ13Ȭ04967Ȭv2
