Simple approach for spectral restoration and super spectral resolution of hyperspectral data

In remote sensing, accurate spectral characteristics are required to identify and describe the targets. Recently, miniature hyperspectral imagers (HySIs) have been flown on small satellites. The non-ideal shape of channels’ spectral response function (SRF) leads to a degraded spectrum, which is normally computed using the band average (BA) method. In this research, the system spectral shape factor (SSSF)-based method is proposed and demonstrated to restore the spectral shape and also to realize super spectral resolution. Computation of spectral radiance (SR) requires channel output and SSSF at that wavelength. SSSF is the convolution of the normalized input signal at a given wavelength and SRF. As the input spectrum is not known prior, coefficients required for SSSF computation are innovatively arrived at from the BA spectrum, which is coarsely similar to the input. The proposed method is tested using SRFs of Chandrayaan-1 and data of Airborne Visible/Infrared Imaging Spectrometer-Next Generation respectively simulating as sensor and input, respectively. The results confirm well matching of SSSF-based spectra with the original with very small deviations in SR values (0.6%), spectral angle map (0.5 deg), and signal information divergence (1×10−5). Slopes remained the same. This study opens up the possibility of optimizing sensor configurations and helps compute accurate spectra from miniature HySIs.