Hyperspectral and multispectral data fusion: A comparative Review of the recent Literature

Abstract

image processing algorithms to enhance the spatial resolution of hyperspectral (HS) imagery. One of the most commonly addressed problems is the fusion of HS data with higherspatial-resolution multispectral (MS) data. Various techniques have been proposed to solve this data fusion problem based on different theories including component substitution, multiresolution analysis, spectral unmixing, and Bayesian probability. This paper presents a comparative review of those HS-MS Fusion techniques with extensive experiments. Ten state-of-the-art HSMS fusion methods are compared by assessing their fusion performance both quantitatively and visually. Eight data sets featuring different geographical and sensor characteristics are used in the experiments to evaluate the generalizability and versatility of the fusion algorithms. To maximize the fairness and transparency of this comparison, publicly available source codes are used, and parameters are individually tuned for maximum performance. Additionally, the impact of spatial resolution enhancement on classification is investigated. Robustness against various factors characterizing the HS-MS fusion problem is systematically analyzed for all methods under comparison. The algorithm characteristics are summarized, and methods with high general versatility are clarified. The paper also provides possible future directions for the development of HS-MS fusion.