Distributed Compressed Sensing Based Remote Sensing Image Fusion Algorithm

LIU Jing; LI Xiaochao; ZHU Kaijian; HUANG Kaiyu

doi:10.11999/JEIT161393

Volume 39 Issue 10

Oct. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(10): 2374-2381

LIU Jing, LI Xiaochao, ZHU Kaijian, HUANG Kaiyu. Distributed Compressed Sensing Based Remote Sensing Image Fusion Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2374-2381. doi: 10.11999/JEIT161393

Citation:

LIU Jing, LI Xiaochao, ZHU Kaijian, HUANG Kaiyu. Distributed Compressed Sensing Based Remote Sensing Image Fusion Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2374-2381. doi: 10.11999/JEIT161393

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Distributed Compressed Sensing Based Remote Sensing Image Fusion Algorithm

doi: 10.11999/JEIT161393

1.
(School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China)
2.

Funds:

The Innovation Foundation of CAST (J20141110), The National Natural Science Foundation of China (61573276), The National 973 Program of China (2013CB329405)

Received Date: 2016-12-23
Rev Recd Date: 2017-06-15
Publish Date: 2017-10-19

Abstract

Abstract

The conventional Compressed Sensing (CS) based remote sensing image fusion algorithm does not consider the correlation between the source images. In this paper, a novel Distributed CS (DCS) based remote sensing image fusion algorithm is proposed to address the correlation between the source images. The proposed algorithm extracts the common part and the unique part of the low frequency information of the source images, in the framework of Joint Sparsity Model-1 (JSM-1). The Unique Feature Addition (UFA) rule is then used to improve the fusion performance. In the experiments, the QuickBird images are utilized to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the fusion performance is significantly improved using the proposed algorithm, compared with several classical fusion algorithms.
- Remote sensing image fusion,
- Distributed Compressed Sensing (DCS),
- Unique Feature Addition (UFA),
- Information correlation

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References(19)

References

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