Hyperspectral cloud shadow removal based on linear unmixing
This work introduces a cloud shadow removal method for hyperspectral images (HSIs) based on hyperspectral unmixing. The shading is modeled by a spectral offset and a spectral-dependent attenuation. The offset and the attenuation are estimated by solving a nonconvex optimization problem, which exploits the linear mixing model (LMM). The mixing matrix of the LMM is estimated from the unshadowed image areas. The effectiveness of the proposed method is assessed from classification results of the Houston 2013 (Compact Airborne Spectrographic Imager (CASI) spectrometer VHR HS), whose shadowed areas were removed with the proposed method. The obtained results indicate classification performances in the shadowed areas very close to those of the unshadowed ones, thus providing evidence of the effectiveness of the proposed shading removal technique.