Remote sensing image fusion based on NSST and parameter adaptive PCNN

Abstract

Remote sensing image fusion aims to enhance the spatial resolution of multispectral (MS) images by extracting spatial detail information from panchromatic (PAN) images. To preserve the spectral information of MS images and enhance the spatial resolution, an innovative remote sensing image fusion method is proposed here. The proposed method is based on an image matting model, the non-subsampled shear wave transform (NSST) and a parameter adaptive pulse coupled neural network (PA-PCNN). In the proposed method, an intensity component, I, is first obtained by adaptively weighted averaging over each band of the MS image. An image matting model is then applied. There, I is used as the α channel to estimate the foreground color, F, and the background color B. Then, the I and PAN images are decomposed into low and high-frequency coefficients by NSST. Two fusion rules are proposed here and applied to fuse the low and high-frequency coefficients, respectively. The fused low and high-frequency coefficients are inversely transformed to get a fused image, which is used as the α channel in the reconstruction of the image. Finally, the F, B and fusion images are combined to reconstruct the final high-resolution fused image. The experimental results show that the proposed approach achieves superior results in both subjective visual effects and objective assessment. It can enhance the spatial resolution and effectively retaining the spectral information of MS images.