基于变分的多尺度遥感图像融合算法

doi:10.3969/j.issn.0372-2112.2020.06.006

电子学报 ›› 2020, Vol. 48 ›› Issue (6): 1084-1090.DOI: 10.3969/j.issn.0372-2112.2020.06.006

基于变分的多尺度遥感图像融合算法

秦福强, 王丽芳

西北工业大学计算机学院, 陕西西安 710029

收稿日期:2018-06-04 修回日期:2019-11-07 出版日期:2020-06-25
- 作者简介:
- 秦福强 男,1980年生于山东日照,西北工业大学计算机专业在读博士,研究方向为遥感大数据分析、图像处理与深度学习和GIS地理信息系统开发等. E-mail:qinfuqiang777@mail.nwpu.edu.cn
  王丽芳 女,1964年出生于黑龙江,西北工业大学工学博士,研究方向为深度学习与自然语言处理、云计算&云安全、计算机视觉等. E-mail:wanglf@nwpu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.41475024）

Multiscale Remote Sensing Image Fusion Algorithm Based on Variational Segmentation

QIN Fu-qiang, WANG Li-fang

School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710029, China

Received:2018-06-04 Revised:2019-11-07 Online:2020-06-25 Published:2020-06-25

摘要/Abstract

摘要： 全色图像与多光谱图像融合时，忽略了上采样的重要性和通道间细节的差异性.针对前者，利用不同尺度下自相似块，估计出低分辨率图像丢失信息，从而修改了图像上采样的策略，并以此构造目标函数的保真项；针对后者，利用全色图像和光谱图像梯度域结构相似性，提出局部加权动态稀疏约束，构造目标函数的正则项.本文基于变分法理论，构造了新的目标函数，并提出了多尺度迭代融合框架，通过多次迭代逐步提高融合图像的分辨率，每一层的融合结果更加准确，从而提高最终的融合精度.本文算法与Brovey等成分替代算法、P+XS等变分算法、MTF_GLP等多分辨分析算法进行比较.实验结果表明，本算法的融合结果具有良好的视觉效果，且在客观评价指标上比所有对比算法的最优值平均值均有提高.

关键词: 多光谱图像, 遥感图像融合, 多尺度自相似性, 局部加权动态梯度稀疏

Abstract: On the fusion of panchromatic and multispectral images,two important aspects,up-sampling of multispectral images and the difference of channel details,are ignored.For the former,the loss details of low-resolution images are estimated by using self-similar patch at different scales to improve up-sampling.For the latter,the local weighted dynamic sparse constraint is proposed based on the structural similarity between panchromatic images and spectral images in gradient domain.The new objective function based on variational method are proposed,the fidelity term and the regularization term of whose are constructed respectively according to the former and the latter.In addition,a multi-scale iterative fusion framework is presented,where the resolution of the fused image is gradually improved through iterations.The fused results of each iteration are more accurate,so the final fused image is improved.Our algorithm is compared with Brovey and other component substitution algorithms,P+XS and other variational algorithms,MTF_GLP and other multi-resolution analysis algorithms.The experimental results show that the fusion results of this algorithm have good visual effect,and the objective evaluation index is better than the average of the optimal value of all comparison algorithms.

Key words: multispectral image, remote sensing image fusion, multiscale self-similarity, local weighted dynamic sparse constraint

中图分类号:

TP751

秦福强, 王丽芳. 基于变分的多尺度遥感图像融合算法[J]. 电子学报, 2020, 48(6): 1084-1090.

QIN Fu-qiang, WANG Li-fang. Multiscale Remote Sensing Image Fusion Algorithm Based on Variational Segmentation[J]. Acta Electronica Sinica, 2020, 48(6): 1084-1090.

参考文献

[1] Ghassemian,Hassan.A review of remote sensing image fusion methods[J].Information Fusion,2016,32:75-89.
[2] Tu T M,Huang P S,Hung C L,et al.A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery[J].IEEE Geoscience and Remote Sensing Letters,2004,1(4):309-312.
[3] Rahmani S,Strait M,et al.An adaptive IHS pan-sharpening method[J].IEEE Geoscience and Remote Sensing Letters,2010,7(4):746-750.
[4] Ghahremani M,Ghassemian H.Nonlinear IHS:a promising method for pan-sharpening[J].IEEE Geoscience and Remote Sensing Letters,2016,13(11):1-5.
[5] Ballester C,Caselles V,Igual L,et al.A variational model for P+XS image fusion[J].International Journal of Computer Vision,2006,69(1):43-58.
[6] Liu,J G.Smoothing filter-based intensity modulation:a spectral preserve image fusion technique for improving spatial details[J].International Journal of Remote Sensing,2000,21(18):3461-3472.
[7] Zhou Z M,Yang P L,Li Y X,et al.Joint IHS and variational methods for pan-sharpening of very high resolution imagery[A].IGARSS 2013[C].Melbourne,VIC,Australia:IEEE,2013.2597-2600.
[8] Chen C,Li Y Q,et al.Image fusion with local spectral consistency and dynamic gradient sparsity[A].2014 Conference on Computer Vision and Pattern Recognition[C].Columbus:IEEE,2014.2760-2765.
[9] Zhu X X,Bamler R.A sparse image fusion algorithm with application to pan-sharpening[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(5):2827-2836.
[10] Li S,Yin H,Fang L.Remote sensing image fusion via sparse representations over learned dictionaries[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(9):4779-4789.
[11] 潘宗序,禹晶,肖创柏,等.基于自适应多字典学习的单幅图像超分辨率算法[J].电子学报,2015,43(2):209-216. Pan Zong-xu,Yu Jin,Xiao Chuang-bo,et al.Single image super-resolution based on adaptive multi-dictionary learning[J].Acta Electronica Sinica,2015,43(2):209-216.(in Chinese)
[12] 宋云,李雪玉,等.基于非局部相似块低秩的压缩感知图像重建算法[J].电子学报,2017,45(3):695-703. Song Yun,Li Xue-yu,et al.Compressed sensing image reconstruction based on low rank of non-local similar patches[J].Acta Electronica Sinica,2017,45(3):695-703.(in Chinese)
[13] Glasner D,Bagon S,Irani M.Super-resolution from a single image[A].2009 IEEE 12th International Conference on Computer Vision[C].Kyoto,Japan:IEEE,2009.349-356.
[14] Gillespie A R.Color enhancement of highly correlated images I.Decorrelation and HSI contrast stretches[J].Remote Sensing of Environment,1986,20(3):209-235.
[15] Alparone L,Baronti S,Selva M.MS+Pan image fusion by enhanced gram-schmidt spectral sharpening[A].Proceedings of the 26th EARSe Symposium,New Strategies for European Remote Sensing[C].Varsovie:EARSe,2006.26-31.
[16] Alparone L,Aiazzi B,Garzelli A,et al.Sharpening of very high resolution images with spectral distortion minimization[A].International Geoscience and Remote Sensing Symposium[C].Toulouse:IEEE,2003.458-460.
[17] Li S,Kang X,Hu J.Image fusion with guided filtering[J].IEEE Transactions on Image Processing,2013,22(7):2864-2875.
[18] Aiazzi B,Alparone L,Baronti S,et al.MTF-tailored multiscale fusion of high-resolution ms and pan imagery[J].Photogrammetric Engineering & Remote Sensing,2006,72(5):591-596.
[19] Choi J,Yu K,Kim Y.A new adaptive component-substitution-based satellite image fusion by using partial replacement[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(1):295-309.

基于变分的多尺度遥感图像融合算法

Multiscale Remote Sensing Image Fusion Algorithm Based on Variational Segmentation

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