A parallel fusion algorithm of multi-spectral image and panchromatic image based on wavelet transform

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Authors:

Xiaorong Xue, The School of Computer and Information Engineering, Anyang Normal University, Anyang, Henan,China

Fang Xiang, The School of Computer and Information Engineering, Anyang Normal University, Anyang, Henan,China

Hongfu Wang, The School of Computer and Information Engineering, Anyang Normal University, Anyang, Henan,China

Jinxi Peng, South China Institute of Software Engineering, Guangzhou university, Guangzhou,China

Abstract:

Purpose. To take the advantages of a variety of remote sensing data, the application of remote sensing image fusion is a very important choice. Remote sensing image fusion is large in computing capacity and time-consuming, and with the development of modern remote sensing technology, the amount of various remote sensing data obtained is getting larger and larger, the issue of how to fuse remote sensing image quickly and accurately and of getting useful information is becoming more and more urgent especially in some remote sensing applications such as disaster monitoring, prevention and relief, etc. In this paper, in order to fuse remote sensing image quickly and accurately, a parallel fusion algorithm of multi-spectral image and panchromatic image based on wavelet transform is proposed.

Methodology. In the method, based on parallel computing, the low-frequency components of wavelet decomposition are fused with the fusion rule based on the feature matching, and the high-frequency components of wavelet decomposition are fused with the fusion rule based on the sub-region variance. Then the low-frequency components and the high-frequency components after fusion are processed with the inverse wavelet transform, and the fused image is obtained.

Findings. The experiment results show that the proposed method can get better fusion results and faster computing speed for multi-spectral image and panchromatic image.

Originality. In the proposed fusion algorithm of multi-spectral image and panchromatic image, wavelet transform and different proper fusion rules for low-frequency components and high-frequency components of wavelet decomposition are used. To get a high speed, parallel computing is also taken in some complex parts of the proposed fusion algorithm. Thus, better fusion results and faster computing speed are obtained.

Practical value. The experiments have proved that the proposed algorithm can quickly get good fusion results for remote sensing images, and it is useful in remote sensing applications in some aspects such as disaster monitoring, prevention and relief, etc.

Список літературы / References

1. Arash Golibagh Mahyari and Mehran Yazdi, 2011. Panchromatic and Multispectral Image Fusion Based on Maximization of Both Spectral and Spatial Similarities. IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 6, pp. 1976–1985.

2. Bo Huang, Huihui Song, Hengbin Cui, Jigen Peng and Zongben Xu, 2014. Spatial and Spectral Image Fusion Using Sparse Matrix Factorization. IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 3, pp. 1693–1704.

3. Fatemeh Tabib Mahmoudi, Farhad Samadzadegan, and Peter Reinartz, Jr., 2014. Object Recognition Based on the Context Aware Decision-Level Fusion in Multiviews Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 1, pp. 12–22.

4. Hamid Reza Shahdoosti and Hassan Ghassemian, 2015. Fusion of MS and PAN Images Preserving Spectral Quality. IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 3 , pp. 611–615.

5. Jaewan Choi, Kiyun Yu, and Yongil Kim, 2011. A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement. IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 1 , pp. 295–309.

6. Hu Xiaodong, Luo Jiancheng, Shen Zhanfeng, Wu Wei and Chen Qiuxiao, 2010. Data sewing algorithm for parallel segmentation of high-resolution remotely sensed image. Journal of Remote Sensing, Vol. 14, No. 5, pp. 917–927.

7. Yang Zhi, Mao Shiyi and Chen Wei, 2005. New image fusion algorithm based on wavelet contrast. Systems Engineering and Electronics, Vol. 27, No. 2, pp. 209–212.

8. Jingjing Ma, Maoguo Gong and Zhiqiang Zhou, 2012. Wavelet Fusion on Ratio Images for Change Detection in SAR Images. IEEE Geoscience And Remote Sensing Letters, Vol. 9, No. 6, pp. 1122–1126.

9. Yong Jiang and Minghui Wang, 2014. Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter. IET Image Process, Vol. 8, No. 3, pp. 183–190.

10. Tong Qingxi and Wei Zheng, 2007. Beijing-1 micro-satellite and its data applications. Spacecraft Engineering, Vol. 16 , No. 2, pp. 1–5.

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