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Mathematical Problems in Engineering
Volume 2013, Article ID 708985, 10 pages
Research Article

A Novel Algorithm for Satellite Images Fusion Based on Compressed Sensing and PCA

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Received 24 May 2013; Accepted 24 June 2013

Academic Editor: Shangbo Zhou

Copyright © 2013 Wenkao Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper studies the image fusion of high-resolution panchromatic image and low-resolution multispectral image. Based on the classic fusion algorithms on remote sensing image fusion, the PCA (principal component analysis) transform, and discrete wavelet transform, we carry out in-depth research. The compressed sensing (CS) abandons the full sample and shifts the sampling of the signal to sampling information that greatly reduces the potential consumption of traditional signal acquisition and processing. We combine compressed sensing with satellite remote sensing image fusion algorithm and propose an innovative fusion algorithm (CS-FWT-PCA), in which the symmetric fractional B-spline wavelet acts as the sparse base. In the algorithm we use Hama Da matrix as the measurement matrix and SAMP as the reconstruction algorithm and adopt an improved fusion rule based on the local variance. The simulation results show that the CS-FWT-PCA fusion algorithm achieves better fusion effect than the traditional fusion method.