Hyperspectral image super-resolution via spectral matching and correction

Fusing a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution RGB image (HR-RGB) is an important technique for HR-HSI obtainment. In this paper, we propose a dual-illuminance fusion-based super-resolution method consisting of spectral matching and correction. In the spectral matching stage, an LR-HSI patch is first searched for each HR-RGB pixel; with the minimum color difference as a constraint, the matching spectrum is constructed by linear mixing the spectrum in the HSI patch. In the spectral correlation stage, we establish a polynomial model to correct the matched spectrum with the aid of the HR-RGBs illuminated by two illuminances, and the target spectrum is obtained. All pixels in the HR-RGB are traversed by the spectral matching and correction process, and the target HR-HSI is eventually reconstructed. The effectiveness of our method is evaluated on three public datasets and our real-world dataset. Experimental results demonstrate the effectiveness of our method compared with eight fusion methods.