Pansharpening based on convolutional autoencoder and multi-scale guided filter

Springer Science and Business Media LLC - Tập 2021 - Trang 1-20 - 2021
Ahmad AL Smadi1, Shuyuan Yang1, Zhang Kai2, Atif Mehmood1, Min Wang3, Ala Alsanabani1
1School of Artificial Intelligence, Xidian University, Xian, China
2School of Information Science and Engineering, Shandong Normal University, Jinan, China
3Key Laboratory of Radar Signal Processing, Xidian University, Xian, China

Tóm tắt

In this paper, we propose a pansharpening method based on a convolutional autoencoder. The convolutional autoencoder is a sort of convolutional neural network (CNN) and objective to scale down the input dimension and typify image features with high exactness. First, the autoencoder network is trained to reduce the difference between the degraded panchromatic image patches and reconstruction output original panchromatic image patches. The intensity component, which is developed by adaptive intensity-hue-saturation (AIHS), is then delivered into the trained convolutional autoencoder network to generate an enhanced intensity component of the multi-spectral image. The pansharpening is accomplished by improving the panchromatic image from the enhanced intensity component using a multi-scale guided filter; then, the semantic detail is injected into the upsampled multi-spectral image. Real and degraded datasets are utilized for the experiments, which exhibit that the proposed technique has the ability to preserve the high spatial details and high spectral characteristics simultaneously. Furthermore, experimental results demonstrated that the proposed study performs state-of-the-art results in terms of subjective and objective assessments on remote sensing data.

Tài liệu tham khảo

K. Zhang, M. Wang, S. Yang, L. Jiao, Spatial–spectral-graph-regularized low-rank tensor decomposition for multispectral and hyperspectral image fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.11(4), 1030–1040 (2018). A. Al Smadi, A. Abugabah, in Proceedings of the 2018 the 2nd International Conference on Video and Image Processing. Intelligent information systems and image processing: a novel pan-sharpening technique based on multiscale decomposition, (2018), pp. 208–212. F. Zhang, K. Zhang, Superpixel guided structure sparsity for multispectral and hyperspectral image fusion over couple dictionary. Multimedia Tools Appl.79(7), 4949–4964 (2020). J. Xu, H. Zhao, P. Yin, D. Jia, G. Li, Remote sensing classification method of vegetation dynamics based on time series Landsat image: a case of opencast mining area in China. EURASIP J. Image Video Process.2018(1), 113 (2018). A. Alsmadi, S. Yang, K. Zhang, Pansharpening via deep guided filtering network. Int. J. Image Process. Vis. Commun.5:, 1–8 (2018). G. Vivone, L. Alparone, J. Chanussot, M. Dalla Mura, A. Garzelli, G. A. Licciardi, R. Restaino, L. Wald, A critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens.53(5), 2565–2586 (2014). L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, L. M. Bruce, Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data-fusion contest. IEEE Trans. Geosci. Remote Sens.45(10), 3012–3021 (2007). A. Mookambiga, V. Gomathi, Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery. Multidim. Syst. Sign. Process.27(4), 863–889 (2016). F. Palsson, J. R. Sveinsson, M. O. Ulfarsson, J. A. Benediktsson, in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Model based pansharpening method based on TV and MTF deblurring (IEEE, 2015), pp. 33–36. W. Li, Y. Li, Q. Hu, L. Zhang, Model-based variational pansharpening method with fast generalized intensity–hue–saturation. J. Appl. Remote. Sens.13(3), 036513 (2019). T. -M. Tu, S. -C. Su, H. -C. Shyu, P. S. Huang, A new look at IHS-like image fusion methods. Inf. Fusion. 2(3), 177–186 (2001). P. Kwarteng, A. Chavez, Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogramm. Eng. Remote Sens.55(1), 339–348 (1989). B. Aiazzi, S. Baronti, M. Selva, Improving component substitution pansharpening through multivariate regression of ms + pan data. IEEE Trans. Geosci. Remote Sens.45(10), 3230–3239 (2007). A. R. Gillespie, A. B. Kahle, R. E. Walker, Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques. Remote Sens. Environ.22(3), 343–365 (1987). J. Liu, Smoothing filter-based intensity modulation: a spectral preserve image fusion technique for improving spatial details. Int. J. Remote Sens.21(18), 3461–3472 (2000). B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, M. Selva, MTF-tailored multiscale fusion of high-resolution MS and Pan imagery. Photogramm. Eng. Remote Sens.72(5), 591–596 (2006). M. M. Khan, J. Chanussot, L. Condat, A. Montanvert, Indusion: fusion of multispectral and panchromatic images using the induction scaling technique. IEEE Geosci. Remote Sens. Lett.5(1), 98–102 (2008). K. He, J. Sun, X. Tang, Guided image filtering. IEEE Trans. Pattern. Anal. Mach. Intell.35(6), 1397–1409 (2012). Y. Yang, W. Wan, S. Huang, F. Yuan, S. Yang, Y. Que, Remote sensing image fusion based on adaptive IHS and multiscale guided filter. IEEE Access. 4:, 4573–4582 (2016). W. Shi, S. Liu, F. Jiang, D. Zhao, Z. Tian, Anchored neighborhood deep network for single-image super-resolution. EURASIP J. Image Video Process.2018(1), 34 (2018). G. Scarpa, S. Vitale, D. Cozzolino, Target-adaptive CNN-based pansharpening. IEEE Trans. Geosci. Remote Sens.56(9), 5443–5457 (2018). S. Huang, J. Wu, Y. Yang, P. Lin, Multi-frame image super-resolution reconstruction based on spatial information weighted fields of experts. Multidim. Syst. Sign. Process.31(1), 1–20 (2020). S. Baghersalimi, B. Bozorgtabar, P. Schmid-Saugeon, H. K. Ekenel, J. -P. Thiran, Dermonet: densely linked convolutional neural network for efficient skin lesion segmentation. EURASIP J. Image Video Process.2019(1), 71 (2019). A. Mehmood, M. Maqsood, M. Bashir, Y. Shuyuan, A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci.10(2), 84 (2020). Y. Wang, H. Bai, L. Zhao, Y. Zhao, Cascaded reconstruction network for compressive image sensing. EURASIP J. Image Video Process.2018(1), 77 (2018). Y. Rao, L. He, J. Zhu, in 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP). A residual convolutional neural network for pan-shaprening (IEEE, 2017), pp. 1–4. W. Huang, L. Xiao, Z. Wei, H. Liu, S. Tang, A new pan-sharpening method with deep neural networks. IEEE Geosci. Remote Sens. Lett.12(5), 1037–1041 (2015). A. Azarang, H. E. Manoochehri, N. Kehtarnavaz, Convolutional autoencoder-based multispectral image fusion. IEEE Access. 7:, 35673–35683 (2019). S. Dolgikh, Spontaneous concept learning with deep autoencoder. Int. J. Comput. Intell. Syst.12(1), 1–12 (2018). W. CARPER, T. LILLESAND, R. KIEFER, The use of intensity-hue-saturation transformations for merging spot panchromatic and multispectral image data. Photogramm. Eng. Remote Sens.56(4), 459–467 (1990). S. Rahmani, M. Strait, D. Merkurjev, M. Moeller, T. Wittman, An adaptive IHS pan-sharpening method. IEEE Geosci. Remote Sens. Lett.7(4), 746–750 (2010). K. He, J. Sun, X. Tang, in European Conference on Computer Vision. Guided image filtering (Springer, 2010), pp. 1–14. C. N. Ochotorena, Y. Yamashita, Anisotropic guided filtering. IEEE Trans. Image Process.29:, 1397–1412 (2019). Y. Bengio, I. Goodfellow, A. Courville, Deep Learning, vol. 1 (MIT Press, Massachusetts, USA, 2017). Y. Song, W. Wu, Z. Liu, X. Yang, K. Liu, W. Lu, An adaptive pansharpening method by using weighted least squares filter. IEEE Geosci. Remote Sens. Lett.13(1), 18–22 (2015). A. Garzelli, F. Nencini, L. Capobianco, Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Trans. Geosci. Remote Sens.46(1), 228–236 (2007). J. Choi, K. Yu, Y. Kim, A new adaptive component-substitution-based satellite image fusion by using partial replacement. IEEE Trans. Geosci. Remote Sens.49(1), 295–309 (2010). G. Masi, D. Cozzolino, L. Verdoliva, G. Scarpa, Pansharpening by convolutional neural networks. Remote Sens.8(7), 594 (2016). M. Imani, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.11(12), 4994–5004 (2018). Z. Wang, A. C. Bovik, A universal image quality index. IEEE Signal Process. Lett.9(3), 81–84 (2002). P. Jagalingam, A. V. Hegde, A review of quality metrics for fused image. Aquat. Procedia. 4:, 133–142 (2015). P. Mhangara, W. Mapurisa, N. Mudau, Comparison of image fusion techniques using satellite pour l’Observation de la Terre (SPOT) 6 satellite imagery. Appl. Sci.10(5), 1881 (2020). G. P. Petropoulos, K. P. Vadrevu, C. Kalaitzidis, Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region. Geocarto Int.28(2), 114–129 (2013). F. Palsson, J. R. Sveinsson, M. O. Ulfarsson, J. A. Benediktsson, Quantitative quality evaluation of pansharpened imagery: consistency versus synthesis. IEEE Trans. Geosci. Remote Sens.54(3), 1247–1259 (2015). L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini, M. Selva, Multispectral and panchromatic data fusion assessment without reference. Photogramm. Eng. Remote Sens.74(2), 193–200 (2008). T. Ranchin, B. Aiazzi, L. Alparone, S. Baronti, L. Wald, Image fusion–the arsis concept and some successful implementation schemes. ISPRS J. Photogramm. Remote. Sens.58(1-2), 4–18 (2003).