Hyperspectral Pansharpening via Local Intensity Component and Local Injection Gain Estimation

Abstract

Hyperspectral (HS) pansharpening is an attractive topic in the field of remote sensing, which has attracted the attention of many researchers. Component substitution (CS)-based HS pansharpening algorithms are of great interest due to their simplicity and high spatial quality, and they mainly consist of two phases: detail extraction and detail injection. Detail extraction is performed by estimating the intensity component, whereas detail injection depends on the definition of injection gain. In the classic CS-based pansharpening methods, the intensity component is estimated through a global synthesis scheme, and injection gains can be obtained by a context-adaptive or a global approach. In this letter, we propose an improved CS-based HS pansharpening method in which the intensity component and the injection gain are estimated locally achieved by the binary partition tree (BPT) image segmentation algorithm. The proposed method is applied to two credible CS-based HS pansharpening algorithms, including the Gram–Schmidt adaptive (GSA) and the Brovey transform (Brovey). The experimental results show that the proposed method improves the performance of GSA and Brovey and creates promising results perceptually and quantitatively.