Remote Sensing Image Fusion Based on Fuzzy Logic and Salience Measure

Abstract

Remote sensing image fusion is to fuse low spatial resolution multispectral (MS) images with high spatial resolution panchromatic (PAN) images to get high spatial resolution multispectral images. The component substitute (CS)-based methods are popular approaches for their high efficiency and high spatial resolution. However, they may produce spectral distortion, especially when there are large radiometric differences between PAN images and MS images. For tackling this problem, a new framework based on the CS model with fuzzy logic and salience measure is proposed. In order to get details that are highly relevant to MS image, a novel fuzzy logic rule based on the global salience measure is designed to fuse the details extracted from both PAN image and MS image. Furthermore, to better preserve the edges of the fused image, a new edge-preserving algorithm is defined to fuse the edges from the PAN image and MS image according to the local salience measure. A series of experiments are conducted and analyzed on both simulated images and real images from the data sets of four satellites to illustrate the effectiveness of the proposed method. Compared with some state-of-the-art methods, our method performs the best in both objective and subjective evaluations. Besides, our method has a low computational cost and is suitable for practical application.