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Volume 45 Issue 7
Jul.  2023
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SONG Jiawen, ZHU Daming, ZUO Xiaoqing, FU Zhitao, CHEN Sijing. A Panchromatic and Multispectral Image Fusion Method Combining Energy and Structural Information[J]. Infrared Technology , 2023, 45(7): 696-704.
Citation: SONG Jiawen, ZHU Daming, ZUO Xiaoqing, FU Zhitao, CHEN Sijing. A Panchromatic and Multispectral Image Fusion Method Combining Energy and Structural Information[J]. Infrared Technology , 2023, 45(7): 696-704.
shu

A Panchromatic and Multispectral Image Fusion Method Combining Energy and Structural Information

  • Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kumming 650093, China

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  • Received Date: April 13, 2023
  • Revised Date: June 19, 2023
    Graphical Abstract
    • Abstract
  • Component substitution is a classical method for remote-sensing image fusion that has good spatial fidelity but is prone to spectral distortion. Therefore, a panchromatic and multispectral image fusion method that combines structural and energy information is proposed. First, the method decomposes the spatial and spectral information of multispectral images by hyperspherical color-space transformation. Second, a two-layer decomposition scheme is introduced through joint bilateral filtering. The panchromatic image and intensity components are then decomposed into structural and energy layers. Finally, the structural layer is fused by the neighborhood spatial frequency strategy, and the pure energy layer of the intensity component is used as the energy layer of the pre-fusion image. The intensity component defines the color intensity. By combining the pre-fused structural layer with the energy layer of the intensity component, the spatial and spectral information of the source image can be effectively combined, thereby reducing the spectral distortion of the pansharpened image. In this study, several experiments were conducted on the Pléiades and QuickBird datasets, and the experimental results were qualitatively and quantitatively analyzed. The results show that the proposed method has certain advantages over existing methods.
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  • [1]
    朱卫东, 王虎, 邱振戈, 等. 自适应多尺度几何分析的全色和多光谱图像融合方法研究[J]. 红外技术, 2019, 41(9): 852-856. http://hwjs.nvir.cn/article/id/hwjs201909009

    ZHU Weidong, WANG Hu, QIU Zhenge, et al. Fusion of panchromatic and multispectral images based on adaptive multiscale geometric analysis method[J]. Infrared Technology, 2019, 41(9): 852-856. http://hwjs.nvir.cn/article/id/hwjs201909009
    [2]
    Vivone G, Dalla Mura M, Garzelli A, et al. A new benchmark based on recent advances in multispectral pansharpening: revisiting pansharpening with classical and emerging pansharpening methods[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(1): 53-81. DOI: 10.1109/MGRS.2020.3019315
    [3]
    LIU P, XIAO L, LI T. A variational pan-sharpening method based on spatial fractional-order geometry and spectral–spatial low-rank priors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(3): 1788-1802. DOI: 10.1109/TGRS.2017.2768386
    [4]
    王欧, 罗小波. 基于细节信息提取的全色与多光谱图像融合方法[J]. 红外技术, 2022, 44(9): 920-928. http://hwjs.nvir.cn/article/id/77831bd5-eda2-46ea-bcdf-82ce141ac5ec

    WANG Ou, LUO Xiaobo. Panchromatic and multispectral images fusion method based on detail information extraction[J]. Infrared Technology, 2022, 44(9): 920-928. http://hwjs.nvir.cn/article/id/77831bd5-eda2-46ea-bcdf-82ce141ac5ec
    [5]
    XIAO J, HUANG T, DENG L, et al. A new context-aware details injection fidelity with adaptive coefficients estimation for variational pansharpening[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-15.
    [6]
    WU Z, HUANG T, DENG L, et al. VO+Net: an adaptive approach using variational optimization and deep learning for panchromatic sharpening[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16.
    [7]
    YANG Z, FU X, LIU A, et al. Progressive pan-sharpening via cross-scale collaboration networks[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.
    [8]
    WU Z, HUANG T, DENG L, et al. A new variational approach based on proximal deep injection and gradient intensity similarity for spatio-spectral image fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 6277-6290. DOI: 10.1109/JSTARS.2020.3030129
    [9]
    Deng L, Vivone G, Paoletti M, et al. Machine learning in pansharpening: a benchmark, from shallow to deep networks[J]. IEEE Geoscience and Remote Sensing Magazine, 2022, 10(3): 279-315. DOI: 10.1109/MGRS.2022.3187652
    [10]
    ZHANG Z Y, HUANG T Z, DENG L J, et al. Pan-sharpening via rog-based filtering[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 2019: 2790-2793. DOI: 10.1109/IGARSS.2019.8899330.
    [11]
    侯昭阳, 吕开云, 龚循强, 等. 一种结合低级视觉特征和PAPCNN的NSST域遥感影像融合方法[J]. 武汉大学学报: 信息科学版, 2023, 48(6): 960-969. DOI: 10.13203/j.whugis20220168.

    HOU Zhaoyang, LÜ Kaiyun, GONG Xunqiang, et al. Remote sensing image fusion based on low-level visual features and PAPCNN in NSST domain[J]. Geomatics and Information Science of Wuhan University, 2023, 48(6): 960-969. DOI: 10.13203/j.whugis20220168
    [12]
    吕开云, 侯昭阳, 龚循强, 等. 一种基于ASR和PAPCNN的NSCT域遥感影像融合方法[J]. 遥感技术与应用, 2022, 37(4): 829-838. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS202204006.htm

    LUY Kaiyun, HOU Zhaoyang, GONG Xunqiang, et al. A remote sensing image fusion method based on ASR and PAPCNN in NSCT domain [J]. Remote Sensing Technology and Application, 2022(4): 829-838. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS202204006.htm
    [13]
    白鑫, 卫琳. 基于IHS变换与自适应区域特征的遥感图像融合算法[J]. 电子测量与仪器学报, 2019, 33(2): 161-167. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201902023.htm

    BAI Xin, WEI Lin. Remote sensing image fusion algorithm based on ihs transform and adaptive region features[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(2): 161-167. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201902023.htm
    [14]
    干林杰, 谭荣建. 一种双尺度分解的高分辨率遥感影像融合方法[J]. 通信技术, 2022, 55(2): 174-180. https://www.cnki.com.cn/Article/CJFDTOTAL-TXJS202202006.htm

    GAN Linjie, TAN Rongjian. A method for fusion of high-resolution remote sensing images based on Dual-Scale decomposition[J]. Communications Technology, 2022, 55(2): 174-180. https://www.cnki.com.cn/Article/CJFDTOTAL-TXJS202202006.htm
    [15]
    Padwick C, Deskevich M, Pacifici F, et al. Worldview-2 pan-sharpening[C]//ASPRS 2010 Annual Conference, 2010: 1-14.
    [16]
    ZHANG Qi, SHEN Xiaoyong, XU Li, et al. Rolling guidance filter[C]//European Conference on Computer Vision (ECCV), 2014: 815-830.
    [17]
    LI X, ZHOU F, TAN H, et al. Multimodal medical image fusion based on joint bilateral filter and local gradient energy[J]. Information Sciences, 2021, 569: 302-325. DOI: 10.1016/j.ins.2021.04.052
    [18]
    Garzelli A, Nencini F, Capobianco L. Optimal MMSE pan sharpening of very high resolution multispectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1): 228-236. DOI: 10.1109/TGRS.2007.907604
    [19]
    Vivone G. Robust band-dependent spatial-detail approaches for panchromatic sharpening[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6421-6433. DOI: 10.1109/TGRS.2019.2906073
    [20]
    Lolli S, Alparone L, Garzelli A, et al. Haze correction for contrast-based multispectral pansharpening[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(12): 2255-2259. DOI: 10.1109/LGRS.2017.2761021
    [21]
    Restaino R, Dalla Mura M, Vivone G, et al. Context-adaptive pansharpening based on image segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 753-766.
    [22]
    MENG X, XIONG Y, SHAO F, et al. A large-scale benchmark data set for evaluating pansharpening performance: overview and implementation[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(1): 18-52.
    [23]
    Aiazzi B, Baronti S, Selva M. Improving component substitution pansharpening through multivariate regression of MS+Pan Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10): 3230-3239.
    [24]
    Khan M M, Chanussot J, Condat L, et al. Indusion: fusion of multispectral and panchromatic images using the induction scaling technique[J]. IEEE geoscience and remote sensing letters, 2008, 5(1): 98-102.
    [25]
    Vivone G, Restaino R, Chanussot J. Full scale regression-based injection coefficients for panchromatic sharpening[J]. IEEE Transactions on Image Processing, 2018, 27(7): 3418-3431.
    [26]
    Vivone G, Restaino R, Chanussot J. A regression-based high-pass modulation pansharpening approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 984-996.
    [27]
    Palsson F, Sveinsson J R, Ulfarsson M O, et al. Model-based fusion of multi- and hyperspectral images using PCA and wavelets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2652-2663.
    [28]
    Roberta H. Yuhas A F H G. Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm[J]. Proc. Summaries 3rd Annu. JPL Airborne Geosci. Workshop, 1992(1): 147-149.
    [29]
    Alparone L, Wald L, Chanussot J, et al. Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data-fusion contest[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10): 3012-3021.
    [30]
    Otazu X, Gonzalez-Audicana M, Fors O, et al. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(10): 2376-2385.
    [31]
    ZHOU Wang, A C Bovik. A universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3): 81-84.
    [32]
    Alparone L, Baronti S, Garzelli A, et al. A global quality measurement of pan-sharpened multispectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2004, 1(4): 313.
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