基于金字塔变换算法优化的遥感图像融合

牛颖超; 周忠发; 谢雅婷; 崔亮

doi:doi:10.3788/lop54.012801

激光与光电子学进展, 2017, 54 (1): 012801, 网络出版: 2017-01-17

基于金字塔变换算法优化的遥感图像融合下载： 558次

Remote Sensing Image Fusion Based on Pyramid Transform Algorithm Optimization

论文大纲

牛颖超 ^1,2,*周忠发 ^1,2谢雅婷 ^1,2崔亮 ³

作者单位

¹ 贵州师范大学喀斯特研究院, 贵州贵阳 550001

² 贵州省遥感中心, 贵州贵阳 550001

³ 贵州北斗空间信息技术有限公司, 贵州贵阳 550001

遥感金字塔变换班德文克隆选择算法算法优化图像融合 remote sensing pyramid transform Baldwinian clonal selection algorithm algorithm optimization image fusion

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摘要

为了弥补金字塔变换算法分解时数据冗余较大、融合结果不理想的缺点, 提出基于金字塔变换算法优化的遥感图像融合新算法。该算法运用金字塔分解构建金字塔序列, 并根据先验知识赋予相应的权重系数, 通过反复迭代重建遥感图像, 再利用班德文克隆选择算法优化选择, 在迭代可承受的范围内, 自适应地修改选择权重系数, 寻求和估计合适的融合参数来优化融合效果, 从而避免金字塔变换算法的经验选择。为了突出本文算法的优点, 实验运用金字塔变换法、遗传算法优化金字塔变换法和粒子群算法优化金字塔变换法进行比较, 从视觉效果和数理统计两个方面分析评价融合质量。实验结果表明, 本文算法更符合人类的视觉感知, 有利于图像的分析和信息的提取。

Abstract

To make up the shortcomings that data redundancy is large and the fusion result is not ideal when pyramid transform algorithm decomposes, a new algorithm of remote sensing image fusion optimized by pyramid transform algorithm is proposed. The proposed algorithm uses pyramid decomposition to build pyramid sequence. According to the relevant weight coefficient given by prior knowledge, the remote sensing image is reconstructed by iterating. Then through optimization selection of Baldwinian clonal selection algorithm, within the acceptable scope of iteration, the weight coefficient is adaptively modified and chose, and the suitable fusion parameter is sought and estimated to optimize fusion effects so as to avoid empirical selection of pyramid transform algorithm. In order to highlight the algorithmic merits, experiment applies pyramid transform optimization, pyramid transform optimization via genetic algorithm, and pyramid transform optimization via particle swarm optimization algorithm to make a comparison. Fusion quality is analyzed and evaluated from the two aspects of visual effect and mathematical statistics. Experimental result indicates that this arithmetic fusion result by the proposed method is consistent with human visual perception, and conducive to image analysis and information extraction.

参考文献

[1] 朱康, 贺新光. 基于Contourlet 系数局部特征的选择性遥感图像融合算法［J］. 激光与光电子学进展, 2012, 49(2): 021001.

Zhu Kang, He Xinguang. Selective remote sensing image fusion method based on the local feature of contourlet coefficients［J］. Laser & Optoelectronics Progress, 2012, 49(2): 021001.

[2] 李晖晖, 郭雷, 刘航. 基于二代curvelet变换的图像融合研究［J］. 光学学报, 2006, 26(5): 657-662.

Li Huihui, Guo Lei, Liu Hang. Research on image fusion based on the second generation curvelet transform［J］. Acta Optica Sinica, 2006, 26(5): 657-662.

[3] 周渝人, 耿爱辉, 王莹, 等. 基于对比度增强的红外与可见光图像融合［J］. 中国激光, 2014, 41(9): 0909001.

Zhou Yuren, Geng Aihui, Wang Ying, et al. Contrast enhanced fusion of infrared and visible images［J］. Chinese J Lasers, 2014, 41(9): 0909001.

[4] 林志垒, 晏路明. 地球观测1号高光谱与全色图像融合的最佳方法［J］. 计算机应用, 2014, 34(8): 2365-2370.

Lin Zhilei, Yan Luming. Best fusion method hyperspectral and panchromatic imagery based on Earth Observing-1 satellite［J］. Journal of Computer Applications, 2014, 34(8): 2365-2370.

[5] Turhan-Sayan G. Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion［J］. IEEE Transactions on Antennas and Propagation, 2005, 53(2): 766-776.

[6] 辛亚楠, 邓磊. 基于小波变换的遥感图像融合方法研究［J］. 激光与光电子学进展, 2013, 50(2): 021001.

Xin Yanan, Deng Lei. An improved remote sensing image fusion method based on wavelet transform［J］. Laser & Optoelectronics Progress, 2013, 50(2): 021001.

[7] 朱瑞辉, 万敏, 范国滨. 基于金字塔变换的图像融合方法［J］. 计算机仿真, 2007, 24(12): 178-180.

Zhu Ruihui, Wan Min, Fan Guobin. An image fuse method based on pyramid transform［J］. Computer Simulation, 2007, 24(12): 178-180.

[8] 杨俊, 赵忠明. 基于HIS变换和亮度调节的遥感图像融合方法［J］. 计算机应用研究, 2007, 24(4): 195-197.

Yang Jun, Zhao Zhongming. Remote sensing image fusion method based on HIS transform and intensity modulation［J］. Application Research of Computers, 2007, 24(4): 195-197.

[9] Gong M, Jiao L, Zhang L. Baldwinian learning in clonal selection algorithm for optimization［J］. Information Sciences, 2010, 180(8): 1218-1236.

[10] Jin H, Jiao L, Liu F, et al. Fusion of infrared and visual images based on contrast pyramid directional filter banks using clonal selection optimizing［J］. Optical Engineering, 2008, 47(2): 027002.

[11] 张新曼, 韩九强. 基于视觉特性的多尺度对比度塔图像融合及性能评价［J］. 西安交通大学学报, 2004, 38(4): 380-383.

Zhang Xinman, Han Jiuqiang. Image fusion of multiscale contrast pyramid-based vision feature and its performance evaluation［J］. Journal of Xi′an Jiaotong University, 2004, 38(4): 380-383.

[12] 李贵喜, 杨万海. 基于多尺度对比度塔的图像融合方法及性能评价［J］. 光学学报, 2001, 21(11): 1336-1342.

Liu Guixi, Yang Wanhai. A multiscale contrast-pyramid-based image fusion scheme and its performance evaluation［J］. Acta Optica Sinica, 2001, 21(11): 1336-1342.

[13] Raghavendra R, Dorizzi B, Rao A, et al. Particle swarm optimization based fusion of near infrared and visible images for improved face verification［J］. Pattern Recognition, 2011, 44(2): 401-411.

[14] 彭开. 基于遗传算法的遥感图像融合方法研究［D］. 西安: 西安电子科技大学, 2010.

Peng Kai. Remote sensing image fusion method based on genetic algorithm［D］. Xi′an: Xidian University, 2010.

[15] 陈荣元, 张飞艳, 张斌, 等. 基于数据同化和粒子群优化算法的遥感影像融合［J］. 电子与信息学报, 2009, 31(10): 2509-2513.

Chen Yongyuan, Zhang Feiyan, Zhang Bin, et al. Remote sensing image fusion based on data assimilation and particle swarm optimization algorithm［J］. Journal of Electronics & Information Technology, 2009, 31(10): 2509-2513.

[16] 代晨阳. 基于蚁群算法的遥感影像分类研究［D］. 福州: 福建师范大学, 2011.

Dai Chenyang. Remote sensing image classification based on ant colony algorithm［D］. Fuzhou: Fujian Normal University, 2011.

[17] Turney P. How to shift bias: Lessons from the baldwin effect［J］. Evolutionary Computation, 2002, 4(3): 271-295.

[18] 张敏辉, 高晓玲, 杨剑. 结合Baldwinian效应和周期变异的免疫函数优化［J］. 计算机工程与应用, 2011, 47(32): 51-53.

Zhang Minhui, Gao Xiaoling, Yang Jian. Function optimization based on immune clone with Baldwinian efficiency and periodically mutation［J］. Computer Engineering and Applications, 2011, 47(32): 51-53.

[19] 杨能. 基于班德文克隆选择算法的模糊聚类研究［D］. 西安: 西安电子科技大学, 2012.

Yang Neng. Based on clonal selection algorithm of fuzzy clustering class German study［D］. Xi′an: Xidian University, 2012.

[20] Huang W, Jing Z. Evaluation of focus measures in multi-focus image fusion［J］. Pattern Recognition Letters, 2007, 28(4): 493-500.

[21] 韩冰, 赵银娣. 一种改进的SFIM高光谱图像融合算法［J］. 遥感信息, 2012, 27(5): 44-47.

Han Bing, Zhao Yindi. An improved smoothing filter-based intensity modulation algorithm for hyperspectral image fusion［J］. Remote Sensing Information, 2012, 27(5): 44-47.

[22] 张勇荣, 周忠发, 马士彬. 基于CBERS-02B星HR图像的多源遥感数据融合研究［J］. 测绘通报, 2012(8): 11-14.

Zhang Yongrong, Zhou Zhongfa, Ma Shibing. Based on the star of CBERS-02B Star HR image of multi-source remote sensing data fusion research［J］. Bulletin of Surveying and Mapping, 2012(8): 11-14.

[23] 杨超, 杨斌, 黄国玉. 基于多光谱图像超分辨率处理的遥感图像融合［J］. 激光与光电子学进展, 2016, 53(2): 021001.

Yang Chao, Yang Bin, Huang Guoyu. Remote sensing image fusion based on multispectral image super-resolution［J］. Laser & Optoelectronics Progress, 2016, 53(2): 021001.

牛颖超, 周忠发, 谢雅婷, 崔亮. 基于金字塔变换算法优化的遥感图像融合[J]. 激光与光电子学进展, 2017, 54(1): 012801. Niu Yingchao, Zhou Zhongfa, Xie Yating, Cui Liang. Remote Sensing Image Fusion Based on Pyramid Transform Algorithm Optimization[J]. Laser & Optoelectronics Progress, 2017, 54(1): 012801.

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