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| Distributed Compressed Sensing Based Remote Sensing Image Fusion Algorithm |
| LIU Jing① LI Xiaochao①② ZHU Kaijian② HUANG Kaiyu① |
①(School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China)
②(State Key Laboratory of Astronautic Dynamics, China Xi'an Satellite Control Center, Xi'an 710043, China) |
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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.
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Received: 23 December 2016
Published: 21 July 2017
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| Fund:The Innovation Foundation of CAST (J20141110), The National Natural Science Foundation of China (61573276), The National 973 Program of China (2013CB329405) |
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Corresponding Authors:
LIU Jing
E-mail: elelj20080730@gmail.com
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