Pan-sharpening based on weighted red black wavelets

Pan-sharpening is a technique which provides an efficient and economical solution to generate multi-spectral (MS) images with high-spatial resolution by fusing spectral information in MS images and spatial information in panchromatic (PAN) image. In this study, the authors propose a new pan-sharpening method based on weighted red-black (WRB) wavelets and adaptive principal component analysis (PCA), where the usage of WRB wavelet decomposition is to extract the spatial details in PAN image and the adaptive PCA is used to select the adequate principal component for injecting spatial details. WRB wavelets are data-dependent second generation wavelets. Multi-resolution analysis (MRA) based on WRB wavelet transform shows a better de-correlation of the data compared with common linear translation-invariant MRA, which makes it suitable for applications requiring manipulating image details. A local processing strategy is introduced to reduce the artefact effects and spectral distortions in the pan-sharpened images. The proposed method is evaluated on the datasets acquired by QuickBird, IKONOS and Landsat-7 ETM + satellites and compared with existing methods. Experimental results demonstrate that the authors method can provide promising fused MS images with high-spatial resolution.