Hyperspectral urban remote sensing image smoothing and enhancement using forward-and-backward diffusion

Anisotropic diffusion has received a lot of attention and has experienced significant developments, with promising results and applications in several specific domains. In this paper, a general flexible class of hyperspectral forward-and-backward (FAB) diffusion process will be proposed, which can achieve the main requirements for edge-preserving regularization with image enhancement. In addition, we use additive operator splitting (AOS) scheme to speedup the numerical evolution of the nonlinear diffusion equation with respect to traditional explicit schemes. The performance of the vector-valued FAB diffusion PDE is studied using one hyperspectral remote sensing image. Experimental results on these images are shown the validity and effectiveness of the proposed method.