Please wait a minute...
 
Remote Sensing for Natural Resources    2025, Vol. 37 Issue (1) : 24-30     DOI: 10.6046/gtzyyg.2023212
|
A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter
XU Xinyu1(), LI Xiaojun1,2,3(), GE Junfei1, LI Yikun1,2,3
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
Download: PDF(5246 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Remote sensing image fusion technology can combine and enhance information from two or more multi-source remote sensing images, making the fused image more accurate and comprehensive. The nonsubsampled contourlet transform (NSCT) is effective in extracting details from high-resolution remote sensing images through multi-scale and multi-directional decomposition, thus achieving image sharpening with high spatial resolution. However, traditional NSCT produces limited high-frequency details and is prone to introduce artifacts such as “ghosting” in fused images. To address this issue, the study proposed a new panchromatic sharpening fusion algorithm for remote sensing images by combining NSCT with a guided filter (GF). Specifically, the promoted algorithm extracted the detail components from histogram-matched images using the multi-scale, multi-direction decomposition and reconstruction properties of the NSCT. Meanwhile, it extracted multi-spectral detail components with panchromatic detail features using GF. Finally, the fused images with high-spatial and high-spectral resolutions were obtained by sharpening based on weighted detail components. The proposed algorithm was proved effective through both subjective and objective evaluations using multiple high-resolution remote sensing datasets.
Keywords nonsubsampled contourlet transform      guided filter      remote sensing image fusion      panchromatic sharpening     
ZTFLH:  TP79  
Issue Date: 17 February 2025
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Xinyu XU
Xiaojun LI
Junfei GE
Yikun LI
Cite this article:   
Xinyu XU,Xiaojun LI,Junfei GE, et al. A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter[J]. Remote Sensing for Natural Resources, 2025, 37(1): 24-30.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2023212     OR     https://www.gtzyyg.com/EN/Y2025/V37/I1/24
Fig.1  Schematic diagram of NSCT secondary decomposition
Fig.2  Flow chart of the proposed pansharpening algorithm
Fig.3  Pansharpening results of WV-2 dataset
Fig.4  Pansharpening results of GF-2 dataset
方法 全分辨率评价 降分辨率评价
Dλ Ds QNR Q4 SAM ERGAS UIQI CC
本文算法 0.020 3 0.034 1 0.946 2 0.705 3 3.533 7 5.238 4 0.725 2 0.861 2
GSA 0.103 0 0.399 9 0.538 3 0.670 6 4.609 1 6.356 8 0.682 9 0.827 7
HPF 0.063 8 0.129 7 0.814 8 0.688 5 3.739 0 5.534 1 0.699 6 0.846 1
SFIM 0.059 3 0.131 1 0.817 4 0.691 0 3.826 1 5.621 6 0.706 8 0.843 3
Indusion 0.042 3 0.109 2 0.853 0 0.588 5 3.979 7 6.725 8 0.601 7 0.777 7
MTF-GLP-HPM-PP 0.113 6 0.268 9 0.648 1 0.701 7 4.115 7 5.616 7 0.714 6 0.847 0
MTF-GLP-CBD 0.042 6 0.132 7 0.830 4 0.698 6 4.293 6 5.740 5 0.711 4 0.849 0
Tab.1  Calculation results of objective evaluation indexes of WorldView-2 image
方法 全分辨率评价 降分辨率评价
Dλ Ds QNR Q4 SAM ERGAS UIQI CC
本文算法 0.034 2 0.062 7 0.905 2 0.802 5 3.855 1 4.230 8 0.798 2 0.868 9
GSA 0.123 3 0.355 7 0.564 9 0.753 6 5.221 9 5.804 6 0.698 9 0.807 7
HPF 0.070 5 0.153 2 0.787 1 0.798 3 3.965 5 4.491 9 0.781 3 0.852 0
SFIM 0.066 7 0.161 4 0.782 7 0.795 2 4.021 1 4.588 0 0.773 3 0.848 4
Indusion 0.041 5 0.112 8 0.850 4 0.655 0 4.326 7 5.957 6 0.626 5 0.734 3
MTF-GLP-HPM-PP 0.119 6 0.267 6 0.644 8 0.798 3 4.523 9 4.661 7 0.764 4 0.848 3
MTF-GLP-CBD 0.061 4 0.183 2 0.766 6 0.781 6 4.861 3 5.265 3 0.739 0 0.831 8
Tab.2  Calculation results of objective evaluation indexes of GF-2 image
[1] Hu J, Hu P, Wang Z, et al. Spatial dynamic selection network for remote-sensing image fusion[J]. IEEE Geoscience and Remote Sensing Letters, 2021,19:8013205.
[2] Cao S Y, Hu X J. Dynamic prediction of urban landscape pattern based on remote sensing image fusion[J]. International Journal of Environmental Technology and Management, 2021, 24(1/2):18.
[3] Xu J, Luo C, Chen X, et al. Remote sensing change detection based on multidirectional adaptive feature fusion and perceptual similarity[J]. Remote Sensing, 2021, 13(15):3053.
[4] Li H, Song D, Liu Y, et al. Automatic pavement crack detection by multi-scale image fusion[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(6):2025-2036.
[5] 盖钧飞, 李小军, 赵鹤婷, 等. 结合脉冲耦合神经网络的自适应全色锐化算法[J]. 测绘科学, 2023, 48(1):60-69.
[5] Ge J F, Li X J, Zhao H T, et al. Adaptive panchromatic sharpening algorithm with pulse coupled neural network[J]. Science of Surveying and Mapping, 2023, 48(1):60-69.
[6] Wady S M A, Bentoutou Y, Bengermikh A, et al. A new IHS and wavelet based pansharpening algorithm for high spatial resolution satellite imagery[J]. Advances in Space Research, 2020, 66(7):1507-1521.
[7] Dadrass Javan F, Samadzadegan F, Mehravar S, et al. A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021,171:101-117.
[8] Wu Z, Huang Y, Zhang K. Remote sensing image fusion method based on PCA and curvelet transform[J]. Journal of the Indian Society of Remote Sensing, 2018, 46(5):687-695.
[9] 张涛, 刘军, 杨可明, 等. 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.
doi: 10.11947/j.AGCS.2015.20140637
[9] Zhang T, Liu J, Yang K M, et al. Fusion algorithm for hyperspectral remote sensing image combined with harmonic analysis and gram-schmidt transform[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9):1042-1047.
doi: 10.11947/j.AGCS.2015.20140637
[10] 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.
[10] Wu Y Q, Wang Z L. Multispectral and panchromatic image fusion using chaotic Bee Colony optimization in NSST domain[J]. Journal of Remote Sensing, 2017, 21(4):549-557.
[11] Jin H, Wang Y. A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization[J]. Infrared Physics & Technology, 2014,64:134-142.
[12] Do M N, Vetterli M. The contourlet transform:An efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing:A Publication of the IEEE Signal Processing Society, 2005, 14(12):2091-2106.
[13] Lim W Q. The discrete shearlet transform:A new directional transform and compactly supported shearlet frames[J]. IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society, 2010, 19(5):1166-1180.
[14] Singh H, Cristobal G, Blanco S, et al. Nonsubsampled contourlet transform based tone-mapping operator to optimize the dynamic range of diatom shells[J]. Microscopy Research and Technique, 2021, 84(9):2034-2045.
[15] 徐欣钰, 李小军, 赵鹤婷, 等. NSCT和PCNN相结合的遥感图像全色锐化算法[J/OL]. 自然资源遥感,[2023-09-18].http://kns.cnki.net/kcms/detail/10.1759.P.20221102.1831.020.html.
url: http://kns.cnki.net/kcms/detail/10.1759.P.20221102.1831.020.html
[15] Xu X Y, Li X J, Zhao H T, et al. Pansharpening algorithm of remote sensing images based on by combining NSCT and PCNN[J]. Remote Sensing for Natural Resources.[2023-09-18].http://kns.cnki.net/kcms/detail/10.1759.P.20221102.1831.020.html.
url: http://kns.cnki.net/kcms/detail/10.1759.P.20221102.1831.020.html
[16] Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform:Theory,design,and applications[J]. IEEE Transactions on Image Processing:A Publication of the IEEE Signal Processing Society, 2006, 15(10):3089-3101.
[17] He K, Sun J, Tang X. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12):2341-2353.
doi: 10.1109/TPAMI.2010.168 pmid: 20820075
[18] He K, Sun J, Tang X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1397-1409.
doi: 10.1109/TPAMI.2012.213 pmid: 23599054
[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.
[20] 张立福, 彭明媛, 孙雪剑, 等. 遥感数据融合研究进展与文献定量分析(1992-2018)[J]. 遥感学报, 2019, 23(4):603-619.
[20] Zhang L F, Peng M Y, Sun X J, et al. Progress and bibliometric analysis of remote sensing data fusion methods(1992-2018)[J]. Journal of Remote Sensing, 2019, 23(4):603-619.
[21] Wald L, Ranchin T, Mangolini M. Fusion of satellite images of different spatial resolutions:Assessing the quality of resulting images[J]. Photogrammetric Engineering and Remote Sensing, 1997, 63(6):691-699.
[22] 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.
[23] 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.
[24] Vivone G, Alparone L, Chanussot J, et al. A critical comparison among pansharpening algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5):2565-2586.
[1] ZHAO Heting, LI Xiaojun, XU Xinyu, GAI Junfei. An ICM-based adaptive pansharpening algorithm for hyperspectral images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 97-104.
[2] XU Xinyu, LI Xiaojun, ZHAO Heting, GAI Junfei. Pansharpening algorithm of remote sensing images based on NSCT and PCNN[J]. Remote Sensing for Natural Resources, 2023, 35(3): 64-70.
[3] MA Ruiqi, CHENG Bo, LIU Xu’nan, LIU Yueming, JIANG Wei, YANG Chen. Research on GF-1 remote sensing IHS image fusion algorithm based on compressed sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 26-32.
[4] XIE Bin, YU Le, ZHANG De-Rong. Distributed Fusion Process for Remotely Sensed Images Based on GIS Services Chain[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(1): 138-142.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech