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Research article

A variational driven optimization framework for pansharpening of multispectral images

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Pages 73-87 | Received 09 Jan 2023, Accepted 09 Nov 2023, Published online: 20 Nov 2023

References

  • Alparone, L., et al., 2008. Multispectral and panchromatic data fusion assessment without reference. Photogrammetric Engineering & Remote Sensing, 74 (2), 193–200. doi:10.14358/PERS.74.2.193.
  • Ballester, C., et al., 2006. A variational model for p+ xs image fusion. International Journal of Computer Vision, 69 (1), 43–58. doi:10.1007/s11263-006-6852-x.
  • Benzenati, T., et al., 2019. Generalized Laplacian pyramid pan-sharpening gain injection prediction based on CNN. IEEE Geoscience and Remote Sensing Letters, 17 (4), 651–655. doi:10.1109/LGRS.2019.2928181.
  • Bingsheng, H., et al., 2002. A new inexact alternating directions method for monotone variational inequalities. Mathematical Programming, 92 (1), 103–118. doi:10.1007/s101070100280.
  • Cheng, M., Wang, C., and Jonathan, L., 2014. Sparse representation based pansharpening using trained dictionary. IEEE Geoscience and Remote Sensing Letters, 11 (1), 293–297. doi:10.1109/LGRS.2013.2256875.
  • Deng, W. and Yin, W., 2016. On the global and linear convergence of the generalized alternating direction method of multipliers. Journal of Scientific Computing, 66 (3), 889–916. doi:10.1007/s10915-015-0048-x.
  • Gogineni, R. and Chaturvedi, A., 2019. A robust pansharpening algorithm based on convolutional sparse coding for spatial enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (10), 4024–4037. doi:10.1109/JSTARS.2019.2945815.
  • Gogineni, R. and Sangani, D.J., 2022. A two-stage pan-sharpening algorithm based on sparse representation for spectral distortion reduction. International Journal of Image and Graphics, 22 (1), 2250007. doi:10.1142/S0219467822500073.
  • Gogineni, R., Chaturvedi, A., and Daya Sagar, B.S., 2021. A variational pansharpening algorithm to enhance the spectral and spatial details. International Journal of Image and Data Fusion, 12 (3), 242–264. doi:10.1080/19479832.2020.1838629.
  • Jiang, C., et al., 2014. Two- step sparse coding for the pan-sharpening of remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (5), 1792–1805. doi:10.1109/JSTARS.2013.2283236.
  • Li, S. and Yang, B., 2011. A new pan-sharpening method using a compressed sensing technique. IEEE Transactions on Geoscience and Remote Sensing, 49 (2), 738–746. doi:10.1109/TGRS.2010.2067219.
  • Liu, P., Xiao, L., and Tao, L., 2017. A variational pan-sharpening method based on spatial fractional-order geometry and spectral–spatial low-rank priors. IEEE Transactions on Geoscience and Remote Sensing, 56 (3), 1788–1802. doi:10.1109/TGRS.2017.2768386.
  • Liu, Q., et al., 2020. Psgan: A generative adversarial network for remote sensing image pan- sharpening. IEEE Transactions on Geoscience & Remote Sensing, 59 (12), 10227–10242. doi:10.1109/TGRS.2020.3042974.
  • Lotfi, M. and Ghassemian, H., 2018. A new variational model in texture space for pansharpening. IEEE Geoscience and Remote Sensing Letters, 15 (8), 1269–1273. doi:10.1109/LGRS.2018.2836951.
  • Ma, J., et al., 2020. Pan-gan: an unsupervised pan-sharpening method for remote sensing image fusion. Information Fusion, 62, 110–120. doi:10.1016/j.inffus.2020.04.006
  • Ma, L., et al., 2019. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. doi:10.1016/j.isprsjprs.2019.04.015
  • Masi, G., et al., 2016. Pansharpening by convolutional neural networks. Remote Sensing, 8 (7), 594. doi:10.3390/rs8070594.
  • Otazu, X., et al., 2005. Introduction of sensor spectral response into image fusion methods. application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing, 43 (10), 2376–2385. doi:10.1109/TGRS.2005.856106.
  • Palsson, F., Sveinsson, J.R., and Ulfarsson, M.O., 2013. A new pansharpening algorithm based on total variation. IEEE Geoscience and Remote Sensing Letters, 11 (1), 318–322. doi:10.1109/LGRS.2013.2257669.
  • Rahmani, S., et al., 2010. An adaptive ihs pan-sharpening method. IEEE Geo- Science and Remote Sensing Letters, 7 (4), 746–750. doi:10.1109/LGRS.2010.2046715.
  • Restaino, R., et al., 2016. Context-adaptive pansharpening based on image segmentation. IEEE Transactions on Geoscience and Remote Sensing, 55 (2), 753–766. doi:10.1109/TGRS.2016.2614367.
  • Rosaria Vicinanza, M., et al., 2015. A pansharpening method based on the sparse representation of injected details. IEEE Geoscience and Remote Sensing Letters, 12 (1), 180–184. doi:10.1109/LGRS.2014.2331291.
  • Sangani, D.J., et al., 2021. Pansharpening of satellite images with convolutional sparse coding and adaptive pcnn-based approach. The Journal of the Indian Society of Remote Sensing, 49 (12), 2989–3004. doi:10.1007/s12524-021-01440-4.
  • Serifoglu Yilmaz, C., Yilmaz, V., and Gungor, O., 2022. A theoretical and practical survey of image fusion methods for multispectral pansharpening. Information Fusion, 79, 1–43. doi:10.1016/j.inffus.2021.10.001
  • Tu, T.-M., et al., 2004. A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1 (4), 309–312. doi:10.1109/LGRS.2004.834804.
  • Vivone, G., et al., 2020. A new benchmark based on recent advances in multispectral pansharpening: revisiting pansharpening with classical and emerging pansharpening methods. IEEE Geoscience and Remote Sensing Magazine, 9 (1), 53–81. doi:10.1109/MGRS.2020.3019315.
  • Wald, L., Ranchin, T., and Mangolini, M., 1997. Fusion of satellite images of diff t spatial resolutions: assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing, 63 (6), 691–699.
  • Xiang Zhu, X. and Bamler, R., 2013. A sparse image fusion algorithm with application to pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing, 51 (5), 2827–2836. doi:10.1109/TGRS.2012.2213604.
  • Yin, H., 2015. Sparse representation based pansharpening with details injection model. Signal Processing, 113, 218–227. doi:10.1016/j.sigpro.2014.12.017

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