Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.

---

Abstract—Compressive sensing (CS) theory is a new type of sampling theory based on information technology. It breaks through the limitations of traditional Nyquist/Shannon sampling theorem, and reconstructs a signal or digital image at a far lower sampling rate. In this paper, we present an efficient remote sensing fusion method based on compressive sensing. First, a sparse model according to the wavelet-based algorithm is used on the panchromatic image and the multispectral image separately. Then the sparse results are compressed through a measurement matrix and different fusion coefficients are chosen on each component of the compressed images. Finally, after reconstruction and invert wavelet transform, we acquire the final fusion image. Compared experiments are made and the simulation results show that the CS fusion algorithm has a more economic and effective performance than the other traditional methods.

Index Terms—Compressive sensing, fusion, measurement matrix, remote sensing, sparse expression

[PDF]

Cite: Duo Wang, GuoJin He, Weili Jiao, "Research on Remote Sensing Image Fusion Based on Compressive Sensing Algorithm," Journal of Computers vol. 13, no. 5, pp. 519-526, 2018.