Remote sensing image fusion based on sparse representation

被引：0

作者：

机构：

[1] School of Aeronautics and Astronautics, Shanghai Jiao Tong University

[2] College of Meteorology, People's Liberation Army University of Science and Technology, Nanjing

来源：

Yin, W. (yinwen@sjtu.edu.cn) | 2013年 / Chinese Optical Society卷 / 33期

关键词：

General component substitution framework; Image fusion; Remote sensing; Sparse representation;

D O I：

10.3788/AOS201333.0428003

中图分类号：

学科分类号：

摘要：

In order to improve multi-spectral (MS) image fusion quality, a new pan-sharpening method based on sparse representation is proposed. A linear regression model between the MS image and its intensity component is established. The sparse coefficients of both panchromatic image and MS image are obtained by two dictionaries which are trained to have the same sparse representations for each high-resolution and low-resolution image patch pair. The coefficient of intensity can also be obtained via the linear regression model and the coefficients of MS bands. Then, the sparse coefficients are fused in the general component substitution (GCOS) fusion framework. The fused sparse coefficients are used to reconstruct a high-resolution MS image. As the inherent characteristics and structure of signals are by via sparse representation more efficiently, the proposed method can preserve spectral and spatial details of the source images well. Experimental results on IKONOS satellite images demonstrate the superiority of the proposed method in both spatial resolution improvement and spectral information preservation.

引用

共 21 条

[1]

Choi M., A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter, IEEE Trans. Geoscience and Remote Sensing, 44, 6, pp. 1672-1682, (2006)

[2]

Chavez P.S., Sides S.C., Anderson J.A., Comparison of three different methods to merge multi-resolution and multispectral data: Landsat TM and SPOT panchromatic, Photogrammetric Engineering & Remote Sensing, 57, 3, pp. 295-303, (1991)

[3]

Zhang Q., Guo B., Fusion of multispectral and panchromatic image based on the physical characteristics of imaging system, Acta Optica Sinica, 27, 2, pp. 243-248, (2007)

[4]

Yocky D.A., Image merging and data fusion by means of the discrete two-dimensional wavelet transform, J. Opt. Soc. Am., 12, 9, pp. 1834-1841, (1995)

[5]

Zhao C., Liu C., Wang K., Research on fusion of hyperspectral remote sensing images based on second generation wavelet, Acta Optica Sinica, 25, 7, pp. 891-896, (2005)

[6]

Yang L., Yang J., Jia H., Et al., Remote sensing images fusion algorithm based on the monsubsampled Contourlet transform, Chinese J. Lasers, 39, (2012)

[7]

Hu J., Li S., Yang B., Remote sensing image fusion based on HIS transform and sparse representation, (2010)

[8]

Li S., Yang B., A new pan-sharpening method using a compressed sensing technique, IEEE Trans. Geoscience and Remote Sensing, 49, 2, pp. 738-746, (2011)

[9]

Dou W., Chen Y., Li X., Et al., A general framework for component substitution image fusion: An implementation using the fast image fusion method source, Computers and Geosciences, 33, 2, pp. 219-228, (2007)

[10]

Chen S., Donoho D., Saunders M., Atomic decomposition by basis pursuit, SIAM J. Sci. Comput., 20, 1, pp. 33-61, (1999)

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