Urban Hyperspectral Image Super-Resolution Combining Subpixel Mapping And Interpolation

Urban hyperspectral image super-resolution can rapidly acquire high-quality data with rich spatial details and spectral fidelity through technical means for urban development. However, conventional super-resolution methods for multi-spectral and natural images struggle to meet the aforementioned data requirements simultaneously. this paper proposes an urban hyperspectral super-resolution method that combines subpixel mapping and interpolation. This method aims to ensure spectral information and minimize time consumption through a high-precision surface interpolation method based on curve theory. Additionally, by utilizing subpixel mapping to introduce urban unmixing information. Finally, by employing wavelet transformation, the method integrates the effective information from both approaches, obtaining urban hyperspectral images with enhanced spatial detail and spectral fidelity. This method has been subjected to comprehensive experimentation, affirming that our proposed method surpasses the current state-of-the-art super-resolution in terms of performance and effectiveness.