FusionNDVI: A Computational Fusion Approach for High-Resolution Normalized Difference Vegetation Index

Abstract

Normalized difference vegetation index (NDVI), derived from the near-infrared and red bands of a multispectral (MS) image, has been widely used in remote sensing. To obtain a high-resolution (HR) NDVI, existing attempts typically first generate an HR-MS image using pansharpening and then calculate the HR NDVI accordingly. However, some inaccurate spatial information will be simultaneously introduced into NDVIs, influencing their spatial quality seriously. To overcome this challenge, we investigate a computational fusion approach from a novel perspective for HR NDVI in this study. Rather than pansharpening an HR-MS image, we define an HR vegetation index calculated based on an available HR panchromatic image and an estimated HR red band (VIPR) and fuse the low-resolution (LR) NDVI and HR VIPR directly to acquire an HR NDVI. In particular, we adopt a nonlocal gradient sparsity constraint to force a similar nonlocal spatial structure in the fused NDVI and VIPR, where the VIPR is dynamically updated by adding a constraint to reconstruct the HR red band. We further integrate a data fidelity term to constrain the relationship between the fused NDVI and its LR version, and an efficient strategy based on the alternative direction multiplier method is developed to solve the nonconvex optimization problem. The extensive experimental results demonstrate that the proposed method achieves superior fusion performance over the state of the art, exhibiting its wide application aspect in remote sensing.