| 混沌蜂群优化的NSST域多光谱与全色图像融合 |
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| 引用本文： | 吴一全,王志来.混沌蜂群优化的NSST域多光谱与全色图像融合[J].遥感学报,2017,21(4):549-557. |
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| 作者姓名： | 吴一全 王志来 |
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| 作者单位： | 南京航空航天大学 电子信息工程学院, 南京 210016;南京信息工程大学 江苏省大数据分析技术重点实验室, 南京 210044;浙江工业大学 浙江省信号处理重点实验室, 杭州 310023;广西师范大学 广西多源信息挖掘与安全重点实验室, 桂林 541004;成都理工大学 国土资源部地学空间信息技术重点实验室, 成都 610059;中国地质科学院矿产资源研究所 国土资源部成矿作用与资源评价重点实验室, 北京 100037,南京航空航天大学 电子信息工程学院, 南京 210016 |
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| 基金项目： | 国家自然科学基金（编号：61573183）；南京信息工程大学，江苏省大数据分析技术重点实验室开放基金（编号：KXK1403）；浙江省信号处理重点实验室开放基金（编号：ZJKL_6_SP-OP2014-02）；广西多源信息挖掘与安全重点实验室开放基金（编号：MIMS16-01）；国土资源部地学空间信息技术重点实验室开放基金（编号：KLGSIT2015-05）；国土资源部成矿作用与资源评价重点实验室开放基金（编号：ZS1406） |
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| 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-HueSaturation(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。
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| 关 键 词： | 图像融合 多光谱与全色图像 非下采样Shearlet变换 混沌蜂群优化 改进的脉冲耦合神经网络 |
| 收稿时间： | 2016/8/12 0:00:00 |
Multispectral and panchromatic image fusion using chaotic Bee Colony optimization in NSST domain |
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| WU Yiquan and WANG Zhilai.Multispectral and panchromatic image fusion using chaotic Bee Colony optimization in NSST domain[J].Journal of Remote Sensing,2017,21(4):549-557. |
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| Authors: | WU Yiquan and WANG Zhilai |
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| Institution: | College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT, Nanjing University of Information Science & Technology, Nanjing 210044, China;Zhejiang Province Key Laboratory for Signal Processing, Zhejiang University of Technology, Hangzhou 310023, China;Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin 541004, China;Key Laboratory of Geo-Spatial Information Technology, Ministry of Land and Resources, Chengdu University of Technology, Chengdu 610059, China;MLR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China and College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| 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. |
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| Keywords: | image fusion multispectral image and panchromatic image non-subsampled shearlet transform chaotic artificial bee colony optimization improved pulse coupled neural network |
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