Abstract
Pan sharpening is pivotal for getting a composite image which consists of the information related to spatial and spectral. In this paper, pan-sharpening method based on co-occurrence filtering with spatial frequency motivated PCNN is proposed. The three parts of panchromatic (PAN) image, i.e., small- and large-scale images, a base image have been obtained through hybrid of co-occurrence and Gaussian filtering (CoF-GF) decomposition. Next, intensity, saturation and hue components of multispectral (MS) image have been obtained through HSI transform. Thirdly, PCNN modulated with spatial frequency has been used to merge the base images and MS image intensity component. Finally, fused output has been reconstructed by an inverse HSI applied on addition of small-scale, large-scale and fused base image. Experiments in three datasets have been validated that the proposed outperforms most of the recently suggested methods.
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References
Ghassemian H (2016) A review of remote sensing image fusion methods. Inf Fusion 32:75–89
Metwalli MR, Nasr AH, Faragallah OS, El-Rabaie ESM, Abbas AM, Alshebeili SA, El-Samie FEA (2014) Efficient pansharpening of satellite images with the contourlet transform. Int J Remote Sens 35(5):1979–2003
Zhou Z, Wang B, Li S, Dong M (2016) Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters. Inf Fusion 30:15–26
Kong W, Zhang L, Lei Y (2014) Novel fusion method for visible light and infrared images based on NSST–SF–PCNN. Infr Phys Technol 65:103–112
Vrabel J (1996) Multispectral imagery band sharpening study. Photogramm Eng Remote Sens 62(9):1075–1084
Masoudi R, KabiriP (2014) New intensity-hue-saturation pan-sharpening method based on texture analysis and genetic algorithm-adaption. J Appl Remote Sens 8(1)
Jelének J, Kopačková V, Koucká L, Mišurec J (2016) Testing a modified PCA-based sharpening approach for image fusion. Remote Sens 8(10):794
Li Q, Yang X, Wu W, Liu K, Jeon G (2018) Pansharpening multispectral remote-sensing images with guided filter for monitoring impact of human behavior on environment. Concurrency Comput Pract Exper p 5074
Ballester C, Caselles V, Igual L, Verdera J, Rougé B (2006) A variational model for P+XS image fusion. Int J Comput Vis 69(1):43–58
Tan W, Xiang P, Zhang J, Zhou H, Qin H (2020) Remote sensing image fusion via boundary measured dual-channel pcnn in multi-scale morphological gradient domain. IEEE Access 8:42540–42549
Jagalingam P, Hegde AV (2015) A review of quality metrics for fused image. Aquatic Procedia 4(Icwrcoe):133–142
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Vanitha, K., Vijay Kumar, K. (2022). Remote Sensing Image Fusion via Hybrid Image Decomposition with Spatial Frequency Motivated PCNN. In: Sivasubramanian, A., Shastry, P.N., Hong, P.C. (eds) Futuristic Communication and Network Technologies. VICFCNT 2020. Lecture Notes in Electrical Engineering, vol 792. Springer, Singapore. https://doi.org/10.1007/978-981-16-4625-6_13
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DOI: https://doi.org/10.1007/978-981-16-4625-6_13
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