Remote Sensing Image Fusion Based on Differential Evolution Algorithm under Data Assimilation Framework

2010 ◽  
Vol 36 (3) ◽  
pp. 392-398 ◽  
Author(s):  
Rong-Yuan CHEN ◽  
Li-Yu LIN ◽  
Si-Chun WANG ◽  
Qian-Qing QIN
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Differential Evolution ◽  
Image Fusion ◽  
Differential Evolution Algorithm ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion ◽  
Evolution Algorithm

Image Fusion Based on Data Assimilation and Differential Evolution Algorithm

2010 ◽  
Vol 39 (9) ◽  
pp. 1688-1692 ◽  
Author(s):  
石良武 SHI Liang-wu ◽  
林立宇 LIN Li-yu ◽  
王四春 WANG Si-chun ◽  
陈荣元 CHEN Rong-yuan
Keyword(s):  
Data Assimilation ◽  
Differential Evolution ◽  
Image Fusion ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm

Data Assimilation Algorithm of Multi-fountain Remote Sensing Image Fusion

2011 ◽  
Vol 37 (3) ◽  
pp. 309-315 ◽  
Author(s):  
Wei FU ◽  
Huan PEI ◽  
Xiao-Yu LIAO ◽  
Chao BAI ◽  
Xian-Wen GAO ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Image Fusion ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion

scholarly journals An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wenping Ma ◽  
Xiafei Fan ◽  
Yue Wu ◽  
Licheng Jiao
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Remote Sensing Image ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Similarity Metric ◽  
Evolution Algorithm ◽  
Orthogonal Learning

We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.

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