Abstract
High spectral correlations and non-local self-similarities, as two intrinsic characteristics underlying hyperspectral image (HSI), have been widely used in HSI super-resolution. However, existing methods mostly utilize the two intrinsic characteristics separately, which still inadequately exploit spatial and spectral information. To address this issue, in this study, a novel self-projected smooth prior (SPSP) is proposed for the task of HSI super-resolution. SPSP describes that two full-band patches (FBPs) are close to each other and then the corresponding subspace coefficients are also close to each other, namely smooth dependences of clustered FBPs within each group of HSI. Suppose that each group of FBPs extracted from HSI lies in smooth subspace, all FBPs within each group can be regarded as the nodes on an undirected graph, then the underlying smooth subspace structures within each group of HSI are implicitly depicted by capturing the linearly pair-wise correlation between those nodes. Utilizing each group of clustered FBPs as projection basis matrix can adaptively and effectively learn the smooth subspace structures. Besides, different from existing methods exploiting non-local self-similarities with multispectral image, to our knowledge, this work represents the first effort to exploit the non-local self-similarities on its spectral intrinsic dimension of desired HSI. In this way, spectral correlations and non-local self-similarities of HSI are incorporated into a unified paradigm to exploit spectral and spatial information simultaneously. As thus, the well learned SPSP is incorporated into the objective function solved by the alternating direction method of multipliers (ADMM). Experimental results on synthetic and real hyperspectral data demonstrate the superiority of the proposed method.
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References
Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Advances in spectral-spatial classification of hyperspectral images. Proc. IEEE 101(3), 652–675 (2013)
Wen, J., Zhao, Y., Zhang, X., Yan, W., Lin, W.: Local discriminant non-negative matrix factorization feature extraction for hyperspectral image classification. Int. J. Remote Sens. 35(13), 5073–5093 (2014)
Yang, J., Zhao, Y.-Q., Chan, J.C.-W.: Learning and transferring deep joint spectral-spatial features for hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 55(8), 4729–4742 (2017)
Liang, J., Zhou, J., Bai, X., Qian, Y.: Salient object detection in hyperspectral imagery. In: 2013 IEEE International Conference on Image Processing, pp. 2393–2397. IEEE (2013)
Wu, K., Xu, G., Zhang, Y., Du, B.: Hyperspectral image target detection via integrated background suppression with adaptive weight selection. Neurocomputing 315, 59–67 (2018)
Lanaras, C., Baltsavias, E., Schindler, K.: Hyperspectral super-resolution by coupled spectral unmixing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3586–3594 (2015)
Yi, C., Zhao, Y.-Q., Chan, J.C.-W.: Hyperspectral image super-resolution based on spatial and spectral correlation fusion. IEEE Trans. Geosci. Remote Sens. 56(7), 4165–4177 (2018)
Zhang, L., Wei, W., Bai, C., Gao, Y., Zhang, Y.: Exploiting clustering manifold structure for hyperspectral imagery super-resolution. IEEE Trans. Image Process. 27(12), 5969–5982 (2018)
Akhtar, N., Shafait, F., Mian, A.: Bayesian sparse representation for hyperspectral image super resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3631–3640 (2015)
Yi, C., Zhao, Y.-Q., Yang, J., Chan, J.C.-W., Kong, S.G.: Joint hyperspectral superresolution and unmixing with interactive feedback. IEEE Trans. Geosci. Remote Sens. 55(7), 3823–3834 (2017)
Yokoya, N., Yairi, T., Iwasaki, A.: Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion. IEEE Trans. Geosci. Remote Sens. 50(2), 528–537 (2012)
Zhao, Y., Yang, J., Chan, J.C.-W.: Hyperspectral imagery super-resolution by spatial-spectral joint nonlocal similarity. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(6), 2671–2679 (2014)
Akhtar, N., Shafait, F., Mian, A.: Sparse spatio-spectral representation for hyperspectral image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 63–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_5
Dian, R., Fang, L., Li, S.: Hyperspectral image super-resolution via non-local sparse tensor factorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5344–5353 (2017)
Xu, Y., Wu, Z., Chanussot, J., Wei, Z.: Nonlocal patch tensor sparse representation for hyperspectral image super-resolution. IEEE Trans. Image Process. 28(6), 3034–3047 (2019)
Dian, R., Li, S.: Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization. IEEE Trans. Image Process. 28(10), 5135–5146 (2019)
Dian, R., Li, S., Fang, L.: Learning a low tensor-train rank representation for hyperspectral image super-resolution. IEEE Trans. Neural Netw. Learn. Syst. 30(9), 2672–2683 (2019)
Bioucas-Dias, J.M., Nascimento, J.M.: Hyperspectral subspace identification. IEEE Trans. Geosci. Remote Sens. 46(8), 2435–2445 (2008)
He, W., Yao, Q., Li, C., Yokoya, N., Zhao, Q.: Non-local meets global: an integrated paradigm for hyperspectral denoising. arXiv preprint arXiv:1812.04243 (2018)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Hu, H., Lin, Z., Feng, J., Zhou, J.: Smooth representation clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3834–3841 (2014)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends R Mach. Learn. 3(1), 1–122 (2011)
Golub, G.H., Van Loan, C.F.: Matrix Computations, vol. 3. JHU press, Baltimore (2012)
Dell’Acqua, F., Gamba, P., Ferrari, A., Palmason, J.A., Benediktsson, J.A., Arnason, K.: Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geosci. Remote Sens. Lett. 1(4), 322–326 (2004)
Yasuma, F., Mitsunaga, T., Iso, D., Nayar, S.: Generalized assorted pixel camera: post-capture control of resolution, dynamic range and spectrum. Technical report, Department of Computer Science, Columbia University CUCS-061-08, November 2008
Wei, Q., Dobigeon, N., Tourneret, J.-Y.: Fast fusion of multi-band images based on solving a Sylvester equation. IEEE Trans. Image Process. 24(11), 4109–4121 (2015)
Simoes, M., Bioucas-Dias, J., Almeida, L.B., Chanussot, J.: A convex formulation for hyperspectral image superresolution via subspace-based regularization. IEEE Trans. Geosci. Remote Sens. 53(6), 3373–3388 (2015)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61371152 and Grant 61771391, in part by the Shenzhen Municipal Science and Technology Innovation Committee under Grant JCYJ20170815162956949 and JCYJ20180306171146740.
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Bu, Y., Zhao, Y., Chan, J.CW. (2020). Hyperspectral Image Super-Resolution via Self-projected Smooth Prior. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_54
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