Abstract
Pan-sharpening aims to generate high-resolution multi-spectral (MS) images by fusing PAN images and low-resolution MS images. Despite its great advances, most existing pan-sharpening methods only work in the spatial domain and rarely explore the potential solutions in the frequency domain. In this paper, we first attempt to address pan-sharpening in both spatial and frequency domains and propose a Spatial-Frequency Information Integration Network, dubbed as SFIIN. To implement SFIIN, we devise a core building module tailored with pan-sharpening, consisting of three key components: spatial-domain information branch, frequency-domain information branch, and dual domain interaction. To be specific, the first employs the standard convolution to integrate the local information of two modalities of PAN and MS images in the spatial domain, while the second adopts deep Fourier transformation to achieve the image-wide receptive field for exploring global contextual information. Followed by, the third is responsible for facilitating the information flow and learning the complementary representation. We conduct extensive experiments to validate the effectiveness of the proposed network and demonstrate the favorable performance against other state-of-the-art methods.
M. Zhou and J. Huang—Co-first authors contributed equally.
F. Zhao—Corresponding author.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Addesso, P., Vivone, G., Restaino, R., Chanussot, J.: A data-driven model-based regression applied to panchromatic sharpening. IEEE Trans. Image Process. 29, 7779–7794 (2020)
Aiazzi, B., Baronti, S., Selva, M.: Improving component substitution pansharpening through multivariate regression of ms \(+ \) pan data. IEEE Trans. Geosci. Remote Sens. 45(10), 3230–3239 (2007)
Alparone, L., Wald, L., Chanussot, J., Thomas, C., Gamba, P., Bruce, L.M.: Comparison of pansharpening algorithms: Outcome of the 2006 grs-s data fusion contest. IEEE Trans. Geosci. Remote Sens. 45(10), 3012–3021 (2007)
Ballester, C., Caselles, V., Igual, L., Verdera, J., Rougé, B.: A variational model for p+ xs image fusion. Int. J. Comput. Vision 69(1), 43–58 (2006)
Cai, J., Huang, B.: Super-resolution-guided progressive pansharpening based on a deep convolutional neural network. IEEE Trans. Geosci. Remote Sens. 59(6), 5206–5220 (2021)
Cao, X., Fu, X., Hong, D., Xu, Z., Meng, D.: PanCSC-net: a model-driven deep unfolding method for pansharpening. IEEE Trans. Geosci. Remote Sens. 1–13 (2021)
Cao, X., Zhou, F., Xu, L., Meng, D., Xu, Z., Paisley, J.: Hyperspectral image classification with Markov random fields and a convolutional neural network. IEEE Trans. Image Process. 27(5), 2354–2367 (2018)
Chen, C., Li, Y., Liu, W., Huang, J.: SIRF: simultaneous satellite image registration and fusion in a unified framework. IEEE Trans. Image Process. 24(11), 4213–4224 (2015)
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)
Frigo, M., Johnson, S.G.: FFTW: an adaptive software architecture for the FFT. In: 1988 International Conference on Acoustics, Speech, and Signal Processing. ICASSP-88, vol. 3 (1998)
Fu, X., Lin, Z., Huang, Y., Ding, X.: A variational pan-sharpening with local gradient constraints. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10265–10274 (2019)
Fu, X., Wang, W., Huang, Y., Ding, X., Paisley, J.: Deep multiscale detail networks for multiband spectral image sharpening. IEEE Trans. Neural Netw. Learn. Syst. 32(5), 2090–2104 (2021)
Fu, Y., Liang, Z., You, S.: Bidirectional 3D quasi-recurrent neural network for hyperspectral image super-resolution. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 14, 2674–2688 (2021)
Fuoli, D., Gool, L.V., Timofte, R.: Fourier space losses for efficient perceptual image super-resolution (2021)
Ghahremani, M., Ghassemian, H.: Nonlinear IHS: a promising method for pan-sharpening. IEEE Geosci. Remote Sens. Lett. 13(11), 1606–1610 (2016)
Gillespie, A.R., Kahle, A.B., Walker, R.E.: Color enhancement of highly correlated images. ii. channel ratio and “chromaticity” transformation techniques - sciencedirect. Remote Sens. Environ. 22(3), 343–365 (1987)
Haut, J.M., Paoletti, M.E., Plaza, J., Li, J., Plaza, A.: Active learning with convolutional neural networks for hyperspectral image classification using a new Bayesian approach. IEEE Trans. Geosci. Remote Sens. 56(11), 6440–6461 (2018)
Haydn, R., Dalke, G.W., Henkel, J., Bare, J.E.: Application of the IHS color transform to the processing of multisensor data and image enhancement. Natl. Acad. Sci. USA 79(13), 571–577 (1982)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
J. R. H. Yuhas, A.F.G., Boardman, J.M.: Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. In: Proceedings of Summaries Annual JPL Airborne Geoscience Workshop, pp. 147–149 (1992)
Jiang, J., Ma, J., Liu, X.: Multilayer spectral-spatial graphs for label noisy robust hyperspectral image classification. IEEE Trans. Neural Netw. Learn. Syst. 1–14 (2020)
Jiang, J., Ma, J., Wang, Z., Chen, C., Liu, X.: Hyperspectral image classification in the presence of noisy labels. IEEE Trans. Geosci. Remote Sens. 57(2), 851–865 (2019)
Jiang, J., Sun, H., Liu, X., Ma, J.: Learning spatial-spectral prior for super-resolution of hyperspectral imagery. IEEE Trans. Comput. Imaging 6, 1082–1096 (2020)
Jiang, K., Wang, Z., Yi, P., Jiang, J.: A progressively enhanced network for video satellite imagery superresolution. IEEE Sig. Process. Lett. 25(11), 1630–1634 (2018)
Jiang, K., et al.: GAN-based multi-level mapping network for satellite imagery super-resolution. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 526–531 (2019)
Khan, M.M., Chanussot, J., Condat, L., Montanvert, A.: Indusion: fusion of multispectral and panchromatic images using the induction scaling technique. IEEE Geosci. Remote Sens. Lett. 5(1), 98–102 (2008)
Kwarteng, P., Chavez, A.: Extracting spectral contrast in Landsat thematic mapper image data using selective principal component analysis. Photogramm. Eng. Remote. Sens. 55(339–348), 1 (1989)
Laben, C., Brower, B.: Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6011875A (2000)
Laben, C.A., Brower, B.V.: Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6,011,875 (2000)
Liao, W., Xin, H., Coillie, F.V., Thoonen, G., Philips, W.: Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter. In: Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (2017)
Liu., J.G.: Smoothing filter-based intensity modulation: a spectral preserve image fusion technique for improving spatial details. Int. J. Remote Sens. 21(18), 3461–3472 (2000)
Lu, X., Zhang, J., Yang, D., Xu, L., Jia, F.: Cascaded convolutional neural network-based hyperspectral image resolution enhancement via an auxiliary panchromatic image. IEEE Trans. Image Process. 30, 6815–6828 (2021)
Ma, J., Xu, H., Jiang, J., Mei, X., Zhang, X.P.: DDCGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process. 29, 4980–4995 (2020)
Ma, J., Yu, W., Chen, C., Liang, P., Guo, X., Jiang, J.: Pan-GAN: an unsupervised pan-sharpening method for remote sensing image fusion. Inf. Fusion 62, 110–120 (2020)
Masi, G., Cozzolino, D., Verdoliva, L., Scarpa, G.: Pansharpening by convolutional neural networks. Remote Sens. 8(7) (2016)
Shah, V.P., Younan, N.H., King, R.L.: An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Trans. Geosci. Remote Sens. 46(5), 1323–1335 (2008)
Tian, X., Chen, Y., Yang, C., Gao, X., Ma, J.: A variational pansharpening method based on gradient sparse representation. IEEE Sig. Process. Lett. 27, 1180–1184 (2020)
Tian, X., Chen, Y., Yang, C., Ma, J.: Variational pansharpening by exploiting cartoon-texture similarities. IEEE Trans. Geosci. Remote Sens. 1–16 (2021)
Tian, X., Li, K., Wang, Z., Ma, J.: VP-Net: an interpretable deep network for variational pansharpening. IEEE Trans. Geosci. Remote Sens. 1–16 (2021)
Vivone, G., et al.: A critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens. 53(5), 2565–2586 (2014)
Wald, L., Ranchin, T., Mangolini, M.: Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images. Photogram. Eng. Remote Sens. 63, 691–699 (1997)
Wang, X., Ma, J., Jiang, J.: Hyperspectral image super-resolution via recurrent feedback embedding and spatial-spectral consistency regularization. IEEE Trans. Geosci. Remote Sens. 1–13 (2021)
Wu, X., Huang, T.Z., Deng, L.J., Zhang, T.J.: Dynamic cross feature fusion for remote sensing pansharpening. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 14687–14696, October 2021
Wu, Z.C., Huang, T.Z., Deng, L.J., Hu, J.F., Vivone, G.: Vo+net: an adaptive approach using variational optimization and deep learning for panchromatic sharpening. IEEE Trans. Geosci. Remote Sens. 1–16 (2021)
Xu, H., Ma, J., Shao, Z., Zhang, H., Jiang, J., Guo, X.: SDPNet: a deep network for pan-sharpening with enhanced information representation. IEEE Trans. Geosci. Remote Sens. 59(5), 4120–4134 (2021)
Xu, S., Zhang, J., Zhao, Z., Sun, K., Liu, J., Zhang, C.: Deep gradient projection networks for pan-sharpening. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1366–1375, June 2021
Yan, K., Zhou, M., Liu, L., Xie, C., Hong, D.: When pansharpening meets graph convolution network and knowledge distillation. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022). https://doi.org/10.1109/TGRS.2022.3168192
Yang, G., Zhou, M., Yan, K., Liu, A., Fu, X., Wang, F.: Memory-augmented deep conditional unfolding network for pan-sharpening. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1788–1797, June 2022
Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., Paisley, J.: PanNet: a deep network architecture for pan-sharpening. In: IEEE International Conference on Computer Vision, pp. 5449–5457 (2017)
Yuan, Q., Wei, Y., Meng, X., Shen, H., Zhang, L.: A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 11(3), 978–989 (2018)
Zhang, H., Ma, J.: GTP-PNet: a residual learning network based on gradient transformation prior for pansharpening. ISPRS J. Photogramm. Remote. Sens. 172, 223–239 (2021)
Zhou, M., Fu, X., Huang, J., Zhao, F., Liu, A., Wang, R.: Effective pan-sharpening with transformer and invertible neural network. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022). https://doi.org/10.1109/TGRS.2021.3137967
Zhou, M., Huang, J., Fang, Y., Fu, X., Liu, A.: Pan-Sharpening with Customized Transformer and Invertible Neural Network. AAAI Press, Palo Alto (2022)
Zhou, M., Xiao, Z., Fu, X., Liu, A., Yang, G., Xiong, Z.: Unfolding Taylor’s approximations for image restoration. In: NeurIPS (2021)
Zhou, M., Yan, K., Huang, J., Yang, Z., Fu, X., Zhao, F.: Mutual information-driven pan-sharpening. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1798–1808 (June 2022)
Zhou, M., Yan, K., Pan, J., Ren, W., Xie, Q., Cao, X.: Memory-augmented deep unfolding network for guided image super-resolution. arXiv abs/2203.04960 (2022)
Acknowledgements
This work was supported by the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, M. et al. (2022). Spatial-Frequency Domain Information Integration for Pan-Sharpening. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-19797-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19796-3
Online ISBN: 978-3-031-19797-0
eBook Packages: Computer ScienceComputer Science (R0)