关键词: 高光谱遥感影像, 光谱隐式函数, 光谱超分辨率

Abstract: [Purposes] Hyperspectral remote sensing imagery is often limited by high acquisition costs and low signal-to-noise ratios.To address these challenges，this paper introduces a Transformer-based spectral implicit function (TSIF) to reconstruct high-quality hyperspectral data from low-spectral-resolution images in a low-cost manner. [Methods] The method models the wavelength-reflectance relationship of each pixel as a continuous function，employs a Transformer-based encoder to fully extract joint spatial-spectral features，and integrates target wavelength information during decoding to achieve fine-grained hyperspectral reconstruction.Additionally，the spectral angle mapper (SAM) term is incorporated into the loss function to enhance the physical consistency of the reconstructed spectra. [Findings] Experiments on two benchmark datasets demonstrate that TSIF achieves superior performance over existing methods，with an improvement of approximately 1 dB in MPSNR and a reduction of more than 0.1 in SAM，confirming its effectiveness in preserving spectral details and recovering spatial structures. [Conclusions] TSIF thus provides an efficient solution for low-cost acquisition of high-quality hyperspectral imagery．

Key words: hyperspectral remote sensing imagery, spectral implicit function, spectral super-resolution