结合自适应PCNN的非下采样剪切波遥感影像融合

doi:10.11947/j.AGCS.2021.20200589

测绘学报 ›› 2021, Vol. 50 ›› Issue (10): 1380-1389.doi: 10.11947/j.AGCS.2021.20200589

结合自适应PCNN的非下采样剪切波遥感影像融合

成飞飞¹, 付志涛¹, 黄亮^1,2, 陈朋弟¹, 黄琨¹

1. 昆明理工大学国土资源工程学院, 云南昆明 650093;
2. 云南省高校高原山区空间信息测绘技术应用工程研究中心, 云南昆明 650093

收稿日期:2020-10-12 修回日期:2021-07-21 发布日期:2021-11-09
通讯作者: 付志涛 E-mail:zhitaofu@kust.edu.cn
作者简介:成飞飞(1994-),男,硕士生,研究方向为多源遥感影像融合。E-mail:642433455@qq.com
基金资助:
国家自然科学基金（41961053）；云南省科技厅基础研究计划面上项目（202101AT070102）；昆明理工大学自然科学研究基金省级人培项目（KKSY201921019）

Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN

CHENG Feifei¹, FU Zhitao¹, HUANG Liang^1,2, CHEN Pengdi¹, HUANG Kun¹

1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China;
2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China

Received:2020-10-12 Revised:2021-07-21 Published:2021-11-09
Supported by:
The National Natural Science Foundation of China(No. 41961053);Yunnan Fundamental Research Project(No. 202101AT070102);Provincial Human Resources Training Project(No. KKSY201921019)

摘要/Abstract

摘要： 为解决全色与多光谱遥感影像融合中脉冲耦合神经网络参数不能自适应调节问题，提出一种基于参数自适应脉冲耦合神经网络模型（PA-PCNN）和保持能量属性（EA）融合策略相结合的非下采样剪切波变换（NSST）的遥感影像融合方法：①通过提取多光谱影像YUV颜色空间变换的Y亮度分量并与全色影像进行NSST变换，获得高频系数和低频系数。②针对低频子带系数，采用EA法进行融合；针对高频子带系数，通过PA-PCNN模型得到的最优参数，以确定最优的PCNN模型，进而实现高频子带系数的融合。③将NSST和YUV进行逆变换得到融合影像。本文选取空间频率、相对无量纲全局误差、相关系数、视觉信息保真度、基于梯度的融合性能和结构相似度测量等6种客观评价指标对融合影像的光谱和空间细节评价，利用多组不同分辨率全色和多光谱遥感影像，通过与4种融合方法对比验证，结果表明本文方法在视觉感知和客观评价方面总体优于其他全色与多光谱遥感影像融合方法。

关键词: 影像融合, 非下采样剪切波变换, 脉冲耦合神经网络, 全色影像, 多光谱影像

Abstract: In order to solve the problem that the parameters of pulse-coupled neural network can't be adjusted adaptively in pan-sharpening image fusion, a non-subsampled shearlet transform remote sensing image fusion method based on the combination of parametric-adaptive pulse coupled neural network model and energy-attributing fusion strategy is proposed. First, the high and low frequency coefficients are obtained by extracting the Y luminance component of the multispectral image YUV color space transform and transforming it with the panchromatic image. Then, aiming at the low-frequency sub-band coefficients are fused by the EA method, the high-frequency sub-band coefficients are obtained by the PA-PCNN model to determine the optimal PCNN model, and then the high-frequency sub-band coefficients are fused; finally, the fusion image is obtained by inverse transformation of NSST and YUV. In this paper, six objective quality indexes, such as spatial frequency, relative dimensionless global error, ERGAS, correlation coefficient, visual information fidelity for fusion, gradient-based fusion performance and structural similarity index, are selected to evaluate the spectral and spatial detail information of the fused images, compared with SE, DGIF, COF and PA-PCNN fusion methods, the proposed method is validated by using multiple sets of high-and low-resolution panchromatic and multispectral remote sensing images, the results show that this method is generally superior to the traditional fusion method of panchromatic and multispectral remote sensing images in objective evaluation and visual perception.

Key words: image fusion, non-subsampled shearlet transform, parameter-adaptive pulse-coupled neural network, panchromatic image, multispectral image

中图分类号:

TP751
P237

成飞飞, 付志涛, 黄亮, 陈朋弟, 黄琨. 结合自适应PCNN的非下采样剪切波遥感影像融合[J]. 测绘学报, 2021, 50(10): 1380-1389.

CHENG Feifei, FU Zhitao, HUANG Liang, CHEN Pengdi, HUANG Kun. Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1380-1389.

参考文献

[1] GHAMISI P, RASTI B, YOKOYA N, et al. Multisource and multitemporal data fusion in remote sensing:a comprehensive review of the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(1):6-39.
[2] 黄波, 赵涌泉. 多源卫星遥感影像时空融合研究的现状及展望[J]. 测绘学报, 2017, 46(10):1492-1499. DOI:10.11947/j.AGCS.2017.20170376. HUANG Bo, ZHAO Yongquan. Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1492-1499.DOI:10.11947/j.AGCS.2017.20170376.
[3] Yun ZHANG. 智能摄影测量和图像处理在高分辨率光学遥感影像处理中的应用:CRC-AGIP实验室的案例[J]. 测绘学报, 2018, 47(6):722-729. DOI:10.11947/j.AGCS.2018.20180011. ZHANG Yun. Smart photogrammetric and remote sensing image processing for optical very high resolution images-examples from the CRC-AGIP lab at UNB[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):722-729.DOI:10.11947/j.AGCS.2018.20180011.
[4] FANG Faming, LI Fang, SHEN Chaomin, et al. A variational approach for pan-sharpening[J]. IEEE Transactions on Image Processing, 2013, 22(7):2822-2834.
[5] 王密, 何鲁晓, 程宇峰, 等. 自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法[J]. 测绘学报, 2018, 47(1):82-90. DOI:10.11947/j.AGCS.2018.20170421. WANG Mi, HE Luxiao, CHENG Yufeng, et al. Panchromatic and multi-spectral fusion method combined with adaptive Gaussian filter and SFIM model[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1):82-90.DOI:10.11947/j.AGCS.2018.20170421.
[6] 王仲妮, 余先川, 张立保. 一种基于整数小波变换的遥感影像融合算法[J]. 光电子·激光, 2008, 19(11):1542-1545. WANG Zhongni, YU Xianchuan, ZHANG Libao. A remote sensing image fusion algorithm based on integer wavelet transform[J]. Journal of Optoelectronics·Laser, 2008, 19(11):1542-1545.
[7] 陈应霞, 陈艳, 刘丛. 遥感影像融合AIHS转换与粒子群优化算法[J]. 测绘学报, 2019, 48(10):1296-1304. DOI:10.11947/j.AGCS.2019.20180509 CHEN Yingxia, CHEN Yan, LIU Cong. Joint AIHS and particle swarm optimization for Pan-sharpening[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10):1296-1304.DOI:10.11947/j.AGCS.2019.20180509。
[8] ZHANG Yun, HONG Gang. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images[J]. Information Fusion, 2005, 6(3):225-234.
[9] DELLEJI T, KALLEL A, BEN HAMIDA A. Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches[J]. International Journal of Remote Sensing, 2014, 35(19):7069-7098.
[10] 李晖晖, 郭雷, 刘坤. 基于曲波变换的遥感图像融合研究[J]. 光电子激光, 2008, 19(3):400-403, 411. LI Huihui, GUO Lei, LIU Kun. Remote sensing image fusion based on curvelet transform[J]. Journal of OptoelectronicsLaser, 2008, 19(3):400-403, 411.
[11] 赵学军, 雷书彧, 滕尚志. 粒子群优化Contourlet变换的遥感影像融合算法[J]. 北京邮电大学学报, 2015, 38(2):118-121. ZHAO Xuejun, LEI Shuyu, TENG Shangzhi. Particle swarm optimization contourlet transform of remote sensing image fusion algorithm research[J]. Journal of Beijing University of Posts and Telecommunications, 2015, 38(2):118-121.
[12] 孙岩, 赵春晖, 江凌. 基于NSCT和PCA变换域的遥感图像融合算法[J]. 沈阳工业大学学报, 2011, 33(3):308-314. SUN Yan, ZHAO Chunhui, JIANG Ling. Remote sensing image fusion algorithm based on NSCT and PCA transform-domain[J]. Journal of Shenyang University of Technology, 2011, 33(3):308-314.
[13] 吴一全, 陶飞翔. 改进投影梯度NMF的NSST域多光谱与全色图像融合[J]. 光学学报, 2015, 35(4):139-148. WU Yiquan, TAO Feixiang. Multispectral and panchromatic image fusion based on improved projected gradient NMF in NSST domain[J]. Acta Optica Sinica, 2015, 35(4):139-148.
[14] SULAIMAN A G, ELASHMAWI W H, EL-TAWEL G S. A robust pan-sharpening scheme for improving resolution of satellite images in the domain of the nonsubsampled shearlet transform[J]. Sensing and Imaging, 2019, 21(1):1-27.
[15] 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. WU Yiquan, 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.
[16] 焦姣, 吴玲达. 形态学滤波和改进PCNN的NSST域多光谱与全色图像融合[J]. 中国图象图形学报, 2019, 24(3):435-446. JIAO Jiao, WU Lingda. Fusion of multispectral and panchromatic images via morphological filter and improved PCNN in NSST domain[J]. Journal of Image and Graphics, 2019, 24(3):435-446.
[17] CONNAH D, DREW M S, FINLAYSON G D. Spectral edge image fusion:Theory and applications[C]//Proceedings of the 13th European Conference on Computer Vision (ECCV)Zurich, SWITZERLAND:ECCV,2014:65-80.
[18] JIAN Lihua, YANG Xiaomin, WU Wei, et al. Pansharpening using a guided image filter based on dual-scale detail extraction[J]. Journal of Ambient Intelligence and Humanized Computing, 2018:1-15.
[19] TAN Wei, XIANG Pei, ZHANG Jiajia, et al. Remote sensing image fusion via boundary measured dual-channel PCNN in multi-scale morphological gradient domain[J]. IEEE Access, 2020, 8:42540-42549.
[20] YIN Ming, LIU Xiaoning, LIU Yu, et al. Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(1):49-64.
[21] ECKHORN R, REITBOCK H J, ARNDT M, et al. A neural network for feature linking via synchronous activity:results from cat visual cortex and from simulations[J]. Models of Brain Function. 1989:255-272.
[22] WANG Zhaobin, MA Yide, CHENG Feiyan, et al. Review of pulse-coupled neural networks[J]. Image & Vision Computing, 2010, 28(1):5-13.
[23] CHEN Yuli, PARK S K, MA Yide, et al. A new automatic parameter setting method of a simplified PCNN for image segmentation[J]. IEEE Transactions on Neural Networks, 2011, 22(6):880-892.
[24] 王昭, 杜庆治, 董安勇, 等. 基于非下采样剪切波变换的红外与可见光图像融合[J]. 光电子·激光, 2020, 31(10):1062-1073. WANG Zhao, DU Qingzhi, DONG Anyong, et al. Infrared and visible image fusion based on nonsubsampled shearlet transform[J]. Journal of Optoelectronics·Laser, 2020, 31(10):1062-1073.
[25] LI Jiayuan, HU Qingwu, AI Mingyao. Multispectral and panchromatic image fusion based on spatial consistency[J]. International Journal of Remote Sensing, 2018, 39(4):1017-1041.
[26] TAN Wei, TIWARI P, PANDEY H M, et al. Multimodal medical image fusion algorithm in the era of big data[J]. Neural Computing and Applications, 2020(3):1-21.

结合自适应PCNN的非下采样剪切波遥感影像融合

Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价

[1]	王密, 何鲁晓, 程宇峰, 常学立. 自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法[J]. 测绘学报, 2018, 47(1): 82-90.
[2]	许宁, 肖新耀, 尤红建, 曹银贵. HCT变换与联合稀疏模型相结合的遥感影像融合[J]. 测绘学报, 2016, 45(4): 434-441.
[3]	张涛, 刘军, 杨可明, 罗文杉, 张育育. 结合Gram-Schmidt变换的高光谱影像谐波分析融合算法[J]. 测绘学报, 2015, 44(9): 1042-1047.
[4]	朱映, 王密, 潘俊, 胡芬. 利用多光谱影像检测资源三号卫星平台震颤[J]. 测绘学报, 2015, 44(4): 399-406.
[5]	赵世湖, 尹丹, 窦显辉, 郭莉. 单景卫星遥感影像目标运动信息提取技术[J]. 测绘学报, 2015, 44(3): 316-322.
[6]	王慧贤江万寿雷呈强张一鸣. 一种基于光谱与空间局部相关的SVR影像融合方法[J]. , 2013, 42(4): 0-0.
[7]	刘军,邵振峰. 基于多尺度光谱增益调制的遥感影像融合方法[J]. 测绘学报, 2011, 40(4): 0-441.