Abstract
In order to fuse two registered multi-spectral (MS) image and panchromatic (PAN) image in the same scene, a new remote sensing image fusion algorithm based on Principal Component Analysis (PCA) and Curvelet transform is proposed. The first principle component PC1 of MS image is extracted via PCA transform, at the same time, we perform the Morphology-Hat transform on the PAN image, and segment the transformed PAN image by the PCNN segmentation algorithm. Perform the Curvelet transform on the component PC1 of MS image and the PAN image after Morphology-Hat transform, and use different fusion rule to fuse different scale layers coefficients (coarse, detail and fine scale layer). For obtaining the fused image, we use the inverse Curvelet transform and inverse PCA transform to obtain the fused image. The experimental results illustrate that the proposed fusion algorithm outperforms Curvelet transform and other traditional fusion algorithms in whole such as intensity–hue–saturation, PCA, Brovey and Weighted Average both in visual effect and objective evaluation indexes (standard deviation, mean, information entropy, correlation coefficient, spectral distortions and deviation index).
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References
Biswas, B., Sen, B. K., & Choudhuri, R. (2015). Remote sensing image fusion using PCNN Model parameter estimation by Gamma distribution in Shearlet domain. Procedia Computer Science, 70, 304–310.
Candes, E., Demanet, L., Donoho, D., & Ying, L. X. (2006). Fast discrete Curvelet transforms. Multiscale Modeling & Simulation Journal, 5(3), 861–899.
Dong, Z., Wang, Z., Liu, D., Zhao, P.,Tang, X., & Jia, M. (2013). SPOT5 multi-spectral (MS) and panchromatic (PAN) image fusion using an improved wavelet method based on local algorithm. Computers & Geosciences, 60(10), 134–141.
Dong, L., Yang, Q., Wu, H., Xiao, H., & Xu, M. (2015). High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform. Neurocomputing, 159, 268–274.
Huang, C., & Bao, W. X. (2014). A remote sensing image fusion algorithm based on the second generation curvelet transform and DS evidence theory. Journal of the Indian Society of Remote Sensing, 42(3), 645–650.
Kong, W. W., Zhang, L. J., & Lei, Y. (2014). Novel fusion method for visible light and infrared images based on NSST-SF-PCNN. Infrared Physics & Technology, 65(7), 103–112.
Li, J., & Lin, Z. J. (1997). Data fusion for remote sensing imagery based on feature. Journal of Image & Graphics, 2(2), 103–107.
Li, H., Liu, L., & Huang, W. (2016). An improved fusion algorithm for infrared and visible images based on multi-scale transform. Infrared Physics & Technology, 74, 28–37.
Liu, Y., Qin, X., Jia, Z., & Yang, J. (2011). Segmentation method of remote sensed images based on principal component analysis and pulse coupled neural network. Computer Engineering & Applications., 47(32), 215–218.
Mitra, S., & Kundu, P. P. (2011). Satellite image segmentation with shadowed C-means. Information Science, 181(17), 3601–3613.
Nencini, F., Garzelli, A., Baronti, S., et al. (2007). Remote sensing image fusion using the curvelet transform. Information Fusion, 8(2), 143–156.
Nirosha, J. J., & Selin, R. M. (2012). Image fusion using PCA in multifeature based Palmprint recognition. International Journal of Soft Computing & Engineering, 2(2), 2231–2307.
Poh, C., & Genderen, J. L. V. (1998). Review article multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823–854.
Smith, L. (2002). A tutorial on principal components analysis. Information Fusion, 51(3), 219–226.
Sulochana, S., Vidhya, R., & Manonmani, R. (2015). Optical image fusion using support value transform (SVT) and curvelets. Optik, 126(18), 1672–1675.
Tu, T. M., Huang, P. S., Hung, C. L., & Chang, C. P. (2004). A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1(4), 309–312.
Wang, Z., Jensen, J. R., & Im, J. (2010). An automatic region-based image segmentation algorithm for remote sensing applications. Environmental Modelling and Software, 25(10), 1149–1165.
Zhang, Y. (1999). A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20(10), 2003–2014.
Zhang, Y. (2004). Understanding image fusion. Photogrammetric Engineering & Remote Sensing, 70(6), 657–661.
Zhang, D., Ji, X., & Wu, B. (2012). An image edge detection method based on mathematical morphology. In Proceedings of ICECC (pp. 981–984).
Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant No. 61261043), Key Science Foundation of North Minzu University (Grant No. 2017KJ36), First-Class Disciplines Foundation of Ningxia (No. NXYLXK2017B09).
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Wu, Z., Huang, Y. & Zhang, K. Remote Sensing Image Fusion Method Based on PCA and Curvelet Transform. J Indian Soc Remote Sens 46, 687–695 (2018). https://doi.org/10.1007/s12524-017-0736-0
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DOI: https://doi.org/10.1007/s12524-017-0736-0