Multispectral and panchromatic image fusion using chaotic Bee Colony optimization in NSST domain

Yiquan WU; Zhilai WANG

doi:10.11834/jrs.20176273

Views : 0 下载量: 1066 CSCD: 11

PDF
Export
Share
Collection
Album

Multispectral and panchromatic image fusion using chaotic Bee Colony optimization in NSST domain
Vol. 21, Issue 4, Pages: 549-557(2017)
Received：12 August 2016，

Accepted：16 January 2017，

Published：2017-07
DOI： 10.11834/jrs.20176273
稿件说明：

移动端阅览

吴一全, 王志来. 2017. 混沌蜂群优化的NSST域多光谱与全色图像融合. 遥感学报, 21(4): 549–557 DOI： 10.11834/jrs.20176273.

Wu Y Q and Wang Z L. 2017. Multispectral and panchromatic image fusion using chaotic Bee Colony optimization in NSST domain. Journal of Remote Sensing, 21(4): 549–557 DOI： 10.11834/jrs.20176273.

摘要

为有效融合多光谱图像的光谱信息和全色图像的空间细节信息，提出了一种基于混沌蜂群优化和改进脉冲耦合神经网络(PCNN)的非下采样Shearlet变换(NSST)域图像融合方法。首先对多光谱图像进行Intensity-Hue-Saturation(IHS)变换，全色图像的直方图按照多光谱图像亮度分量的直方图进行匹配；然后分别对多光谱图像的亮度分量和新全色图像进行NSST变换，对低频分量使用改进加权融合算法进行融合，以互信息作为适应度函数，利用混沌蜂群算法找到最优加权系数。对高频分量采用改进脉冲耦合神经网络(PCNN)方法进行融合，再经NSST逆变换和IHS逆变换得到融合图像。本文方法在主观视觉效果和信息熵、光谱扭曲度等客观定量评价指标上优于基于IHS变换、基于非下采样Contourlet变换(NSCT)和非负矩阵分解(NMF)、基于NSCT和PCNN等5种融合方法。本文方法在提升图像空间分辨率的同时，有效地保留了光谱信息。

Abstract

The rapid development of remote sensing technology provides an effective technical approach for humans to understand living environments and to utilize natural resources. Various remote sensing sensors exist

and the images formed by different image sensors have various characteristics

thereby resulting in multi-source remote sensing images

such as multi-spectral and panchromatic images. Fusing the multi-source remote sensing images of the same scene is necessary to efficiently and comprehensively deal with these image data. The multi-spectral image has high spectral resolution and rich spectral information; however

the spatial resolution of this image is low due to the limitations of physical devices. The panchromatic image has high spatial resolution and clear spatial detail; however

its spectral resolution is low. The fusion of multi-spectral and panchromatic images is the integration of the spatial detail information of the panchromatic image into the multi-spectral image to generate an image with high spatial resolution and spectral resolution

which benefit subsequent image processing. A method for the fusion of multi-spectral and panchromatic images using chaotic artificial bee colony optimization and improved Pulse Coupled Neural Network (PCNN) in a Non-Subsampled Shearlet Transform (NSST) domain is proposed. First

Intensity Hue Saturation (IHS) transform is performed on the multi-spectral image. The histogram of the panchromatic image is matched to the histogram of the intensity component of the multi-spectral image. The intensity component of the multispectral image and the new panchromatic image are then decomposed by NSST. Next

the low-frequency component is fused with the improved weighted fusion algorithm. Recently

artificial bee colony algorithm is one of the effective swarm intelligence optimization algorithms

which can adaptively determine the weighted coefficient. Chaotic bee colony optimization algorithm is designed by introducing the tent mapping chaotic sequence to avoid premature phenomena. The chaotic bee colony optimization algorithm has high convergence precision and rapid convergence speed in global optimization. By exploiting this property

the optimal improved weighted coefficient is determined by the chaotic artificial bee colony optimization algorithm. Mutual information is used as the fitness function. The improved PCNN method is adopted for the fusion of high-frequency components. Finally

the fused image is obtained by inverse NSST and inverse IHS transform. Many multi-spectral and panchromatic remote sensing images from LANDSAT TM

IKONOS

and SPOT 4 satellites are tested. Qualitative and quantitative evaluation results are obtained to verify the feasibility and effectiveness of the proposed method. The proposed method outperforms five other kinds of fusion methods: the IHS method

the method of Non-Subsampled Contourlet Transform (NSCT) combined with Non-negative Matrix Factorization (NMF)

the method of NSCT combined with PCNN in the subjective visual effect

and the objective quantitative evaluation indexes

such as information entropy and spectral distortion. The proposed method can effectively preserve the spectral information of the multispectral image

while the details of panchromatic images are injected into the fused image as much as possible

effectively improving the spatial resolution of the fused image.

关键词

Keywords

references

安振宇, 史振威. 2013. 基于sc-NMF的高光谱图像融合. 红外与激光工程, 42(10): 2718–2723

An Z Y and Shi Z W. 2013. Hyperspectral image fusion via sc-NMF. Infrared and Laser Engineering, 42(10): 2718–2723

Blasch E P. 1999. Biological information fusion using a PCNN and belief filtering//International Joint Conference on Neural Networks. Washington, DC: IEEE: 2792–2795 [DOI: 10.1109/IJCNN.1999. 833523]

Chien C L and Tsai W H. 2014. Image fusion with no gamut problem by improved nonlinear IHS transforms for remote sensing. IEEE Transactions on Geoscience Remote Sensing, 52(1): 651–663

Da Cunha A L, Zhou J P and Do M N. 2006. The nonsubsampled contourlet transform: theory, design, and applications. IEEE Transactions on Image Processing, 15(10): 3089–3101

Dong L M, Yang Q X, Wu H Y, Xiao H C and Xu M L. 2015. High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform. Neurocomputing, 159(c): 268–274

Easley G, Labate D and Lim W Q. 2008. Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis, 25(1): 25–46

Gao G R, Xu L P and Feng D Z. 2013. Multi-focus image fusion based on non-subsampled shearlet transform. IET Image Processing, 7(6): 633–639

Guo K H and Labate D. 2007. Optimally sparse multidimensional representation using shearlets. Siam Journal on Mathematical Analysis, 39(1): 298–318

郭雷, 程塨, 赵天云. 2011. 基于小波变换和邻域特征的多聚焦图像融合算法. 西北工业大学学报, 29(3): 454–459

Guo L, Gong C and Zhao T Y. 2011. A new and effective multi-focus image fusion algorithm based on wavelet transforms and neighborhood features. Journal of Northwestern Polytechnical University, 29(3): 454–459

蒋年德, 王耀南, 毛建旭. 2008. 基于Curvelet变换的遥感图像融合研究. 仪器仪表学报, 29(1): 61–66

Jiang N D, Wang Y N and Mao J X. 2008. Research on remote sensing image fusion based on Curvelet transform. Chinese Journal of Scientific Instrument, 29(1): 61–66

Johnson J L and Padgett M L. 1999. PCNN models and applications. IEEE Transactions on Neural Networks, 10(3): 480–498

Karaboga D and Basturk B. 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3): 459–471

Kong W W, Wang B H and Lei Y. 2015. Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model. Infrared Physics and Technology, 71: 87–98

李新娥, 任建岳, 吕增明, 沙巍, 张立国, 何斌. 2013. NSCT域内基于改进PCNN和区域能量的多光谱和全色图像融合方法. 红外与激光工程, 42(11): 3096–3102

Li X E, Ren J Y, Lv Z M, Sha W, Zhang L G and He B. 2013. Fusion method of multispectral and panchromatic images based on improved PCNN and region energy in NSCT domain. Infrared and Laser Engineering, 42(11): 3096–3102

廖勇, 黄文龙, 尚琳, 李鹏. 2014. Shearlet与改进PCNN相结合的图像融合. 计算机工程与应用, 50(2): 142–146

Liao Y, Huang W L, Shang L and Li P. 2014. Image fusion based on Shearlet and improved PCNN. Computer Engineering and Applications, 50(2): 142–146

刘坤, 李晖晖. 2013. 基于Contourlet域HMT似然概率的图像融合方法. 光电子: 激光, 24(10): 2031–2037

Liu K and Li H H. 2013. An image fusion algorithm based on likelihood function of HMT in Contourlet domain. Journal of Optoelectronics: Laser, 24(10): 2031–2037

宋梦馨, 郭平. 2012. 结合Contourlet和HSI变换的组合优化遥感图像融合方法. 计算机辅助设计与图形学学报, 24(1): 83–88

Song M X and Guo P. 2012. A combinatorial optimization method for remote sensing image fusion with Contourlet and HSI transform. Journal of Computer-Aided Design and Computer Graphics, 24(1): 83–88

Wu Y Q, Wu C and Wu S H. 2012. Fusion of multispectral image and panchromatic image based on NSCT and NMF. Journal of Beijing Institute of Technology, 21(3): 415–420

夏余, 曲仕茹. 2014. 改进(2D) 2 PCA的彩色图像融合框架. 光学学报, 34(10): 69–76

Xia Y and Qu S R. 2014. Color image fusion framework based on improved (2D) 2 PCA. Acta Optica Sinica, 34(10): 69–76

颜建军, 夏春明, 郑建荣. 2007. 基于NMF的多光谱图像和全色图像融合方法. 计算机工程, 33(21): 169–171

Yan J J, Xia C M and Zheng J R. 2007. Multi-spectral image and panchromatic image fusion method based on non-negative matrix factorization. Computer Engineering, 33(21): 169–171

Zhang B H, Zhang C, Liu Y Y, Wu J S and He L. 2014. Multi-focus image fusion algorithm based on compound PCNN in Surfacelet domain. Optik-International Journal for Light and Electron Optics, 125(1): 296–300

Alert me when the article has been cited

提交

Spectral-frequency domain attribute pattern fusion for hyperspectral image change detection

Inversion of LST and extraction of silicification alteration informationbased on the weighted fusion TIR data： A case study of the Uranium Polymetallic mining area in RenCha basin of Guangdong province

Neural network generation of adaptive filter and new applications in remote sensing image processing

Detail focused fusion of hyperspectral and multispectral images

Development status and future prospects of multi-source remote sensing image fusion