An Efficient Pansharpening Method Based On Conditional Random Fields

Abstract

Pansharpening is to fuse the existing low spatial resolution multi-spectral (MS) image with high spatial resolution panchromatic (PAN) image, so as to obtain high spatial resolution MS (HRMS) image. An efficient pansharpening model based on conditional random fields (CRFs) is proposed in this paper. In the model, a state feature function is designed to force the blurred HRMS image in accordance with the upsampled MS (UPMS) image to keep the spectral fidelity. Meanwhile, a transition feature function is defined to keep the sharpness of fused image. Besides, a new Gaussian filter acquisition algorithm is proposed to effectively satisfy the blur function in the model. To improve the efficiency of algorithm, a new initialization method based on fitting normal distribution is presented. Experiments are conducted on both reduced-scale images and full-scale images. Compared with some classical and state-of-art pansharpening methods, the proposed method achieves the best results in terms of fusion quality and efficiency.