Full-Spectrum Spectral Super-Resolution Method Based on LSMM

Abstract

Full-spectrum remote sensing images can simultaneously provide reflectance and emission information about objects, which has great application value. Hyperspectral imaging can record hundreds of spectral bands, but due to technical and space limitations, full-spectrum hyperspectral images (HSI) are difficult to obtain. Recently, we proposed a spectral super-resolution method based on the Linear Spectral Mixing Model (LSMM), which can generate full-spectrum hyperspectral images (HSI) from multispectral images (MSI). After the spectral-unmixing of MSI, we transform MS endmembers into full-spectrum HS endmembers by spectral library. Since the abundance of MSI and HSI with the same spatial resolution is consistent, we linearly mixed the abundance and HS endmember to obtain the full spectrum HSI. In this work, we use Sentinel-2 dataset and EO-1 ALI/Hyperion images to verify the accuracy and applicability. Compared with other works, our method can simulate full-spectrum HSI of large-area scenes without real HSI, which has a certain accuracy and provides more comprehensive information for applications.