Abstract
Spectral images captured by satellites and radio-telescopes are analyzed to obtain information about geological compositions distributions, distant asters as well as undersea terrain. Spectral images usually contain tens to hundreds of continuous narrow spectral bands, so the visualization based on information fusion and dimensional reduction is required for proper display on a trichromatic monitor which is important for spectral image processing and analysis system. The visualizations of spectral images should preserve as much information as possible from the original signal and facilitate image interpretation. However, most of the existing visualization methods display spectral images in false colors, which contradicts with human’s expectation and experience. In this paper, we present a novel visualization method based on generative adversarial network (GAN) to display spectral images in natural colors, in which a structure loss and an adversarial loss are combined to form a new loss function. The adversarial loss fits the visualized image to the natural image distribution using a discriminator network that is trained to distinguish false-color images from natural-color images. At the same time, we use an improved cycle loss as the structure constraint to guarantee structure consistency. Experimental results show that our method is able to generate structure-preserved and natural-looking visualizations.
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References
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. ArXiv e-prints, January 2017
Bachmann, C.M., Ainsworth, T.L., Fusina, R.A.: Exploiting manifold geometry in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 43(3), 441–454 (2005)
Connah, D., Drew, M.S., Finlayson, G.D.: Spectral edge image fusion: theory and applications. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014 Part V. LNCS, vol. 8693, pp. 65–80. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_5
Cui, M., Razdan, A., Hu, J., Wonka, P.: Interactive hyperspectral image visualization using convex optimization. IEEE Trans. Geosci. Remote Sens. 47(6), 1673–1684 (2009)
Goodfellow, I.J., et al.: Generative adversarial networks. arXiv:Machine Learning (2014)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. ArXiv e-prints, March 2017
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. ArXiv e-prints, December 2015
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. ArXiv e-prints, April 2017
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(<\)0.5MB model size. arXiv:1602.07360 (2016)
Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Computer Vision and Pattern Recognition, pp. 1125–1134 (2016)
Jacobson, N.P., Gupta, M.R.: Design goals and solutions for display of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 43(11), 2684–2692 (2005)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kotwal, K., Chaudhuri, S.: Visualization of hyperspectral images using bilateral filtering. IEEE Trans. Geosci. Remote Sens. 48(5), 2308–2316 (2010)
Kotwal, K., Chaudhuri, S.: An optimization-based approach to fusion of hyperspectral images. IEEE J. Sel. Topics Appl. Earth Obs. Rem. Sens. 5(2), 501–509 (2012)
Liao, D., Qian, Y., Zhou, J.: Visualization of hyperspectral imaging data based on manifold alignment. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 70–75. IEEE (2014)
Liao, D., Qian, Y., Zhou, J., Tang, Y.Y.: A manifold alignment approach for hyperspectral image visualization with natural color. IEEE Trans. Geosci. Remote Sens. 54(6), 3151–3162 (2016)
Liao, D., Ye, M., Jia, S., Qian, Y.: Visualization of hyperspectral imagery based on manifold learning. In: 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1979–1982. IEEE (2013)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Mignotte, M.: A multiresolution Markovian fusion model for the color visualization of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 48(12), 4236–4247 (2010)
Mignotte, M.: A bicriteria-optimization-approach-based dimensionality-reduction model for the color display of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 50(2), 501–513 (2012)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. ArXiv e-prints, November 2014
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations (2016)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. ArXiv e-prints, June 2016
Su, H., Du, Q., Du, P.: Hyperspectral image visualization using band selection. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 7(6), 2647–2658 (2014)
Tyo, J.S., Konsolakis, A., Diersen, D.I., Olsen, R.C.: Principal-components-based display strategy for spectral imagery. IEEE Trans. Geosci. Remote Sens. 41(3), 708–718 (2003)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. ArXiv e-prints, July 2016
Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: unsupervised dual learning for image-to-image translation. ArXiv e-prints, April 2017
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)
Zhu, Y., Varshney, P.K., Chen, H.: Evaluation of ICA based fusion of hyperspectral images for color display, pp. 1–7 (2007)
Acknowledgement
This work was supported by the National Natural Science Foundation of China 61571393, and the National Key Research and Development Program of China 2018YFB0505000.
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Chen, S., Liao, D., Qian, Y. (2018). Spectral Image Visualization Using Generative Adversarial Networks. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_30
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