Pansharpening using regression of classified MS and pan images to reduce color distortion

The synthesis of low-resolution panchromatic (Pan) image is a critical step of ratio enhancement (RE) and component substitution (CS) pansharpening methods. The two types of methods assume a linear relation between Pan and multispectral (MS) images. However, due to the nonlinear spectral response of satellite sensors, the qualified low-resolution Pan image cannot be well approximated by a weighted summation of MS bands. Therefore, in some local areas, significant gray value difference exists between a synthetic Pan image and a high-resolution Pan image. To tackle this problem, the pixels of Pan and MS images are divided into several classes by $k$-means algorithm, and then multiple regression is used to calculate summation weights on each group of pixels. Experimental results demonstrate that the proposed technique can provide significant improvements on reducing color distortion.