A Multispectral and Panchromatic Images Fusion Method Based on Weighted Mean Curvature Filter Decomposition

Since the hardware limitations of satellite sensors, the spatial resolution of multispectral (MS) images is still not consistent with the panchromatic (PAN) images. It is especially important to obtain the MS images with high spatial resolution in the field of remote sensing image fusion. In order to obtain the MS images with high spatial and spectral resolutions, a novel MS and PAN images fusion method based on weighted mean curvature filter (WMCF) decomposition is proposed in this paper. Firstly, a weighted local spatial frequency-based (WLSF) fusion method is utilized to fuse all the bands of a MS image to generate an intensity component IC. In accordance with an image matting model, IC is taken as the original alpha channel for spectral estimation to obtain a foreground and background images. Secondly, a PAN image is decomposed into a small-scale (SS), large-scale (LS) and basic images by weighted mean curvature filter (WMCF) and Gaussian filter (GF). The multi-scale morphological detail measure (MSMDM) value is used as the inputs of the Parameters Automatic Calculation Pulse Coupled Neural Network (PAC-PCNN) model. With the MSMDM-guided PAC-PCNN model, the basic image and IC are effectively fused. The fused image as well as the LS and SS images are linearly combined together to construct the last alpha channel. Finally, in accordance with an image matting model, a foreground image, a background image and the last alpha channel are reconstructed to acquire the final fused image. The experimental results on four image pairs show that the proposed method achieves superior results in terms of subjective and objective evaluations. In particular, the proposed method can fuse MS and PAN images with different spatial and spectral resolutions in a higher operational efficiency, which is an effective means to obtain higher spatial and spectral resolution images.