Spectral-Driven Pansharpening Using Adaptive Image Segmentation to Reduce Color Distortion

Extraction and injection of spatial details from the panchromatic (PAN) image and preservation of the spectral characteristics of the multispectral (MS) image are the key issues in pansharpening methods, which are of great significance for the application of image interpretation and target recognition. In this paper, a spectral-driven pansharpening method in which the injection detailed coefficients are estimated over each component segmented by the adaptive K-means algorithm is proposed. The pansharpening method relies on a multi-resolution framework, generalized Laplacian pyramid (GLP) technique, which is applied for the extraction of detail image. The fused image is used as the feedback element, its distance from the original MS image is used to adjust the number of segments adaptively so as to reduce the spectral distortion. Experiments carried out on GeoEye-1 and QuickBird data sets demonstrate the efficiency and effectiveness of our proposed method.