Abstract
In 2015, the Gough Map was imaged using a hyperspectral imaging system while in the collection at the Bodleian Library, University of Oxford. It is one of the earliest surviving maps of Britain. Hyperspectral image (HSI) classification has been widely used to identify materials in remotely sensed images. Recently, hyperspectral imaging has been applied to historical artifact studies. The collection of the HSI data of the Gough Map was aimed at pigment mapping for towns and writing with different spatial patterns and spectral (color) features. We developed a spatial-spectral deep learning framework called 3D-SE-ResNet to automatically classify pigments in large HSI of cultural heritage artifacts with limited reference (labelled) data and have applied it to the Gough Map. With much less effort and much higher efficiency, this is a breakthrough in object identification and classification in cultural heritage studies that leverages the spectral and spatial information contained in this imagery, providing codicological information to cartographic historians.
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References
Bai, D.: A hyperspectral image classification approach to pigment mapping of historical artifacts using deep learning methods. Ph.D. dissertation, Rochester Institute of Technology (2019)
Bai, D., Messinger, D.W., Howell, D.: Hyperspectral analysis of cultural heritage artifacts: pigment material diversity in the Gough map of Britain. Opt. Eng. 56(8), 081805–081805 (2017)
Bai, D., Messinger, D.W., Howell, D.: A pigment analysis tool for hyperspectral images of cultural heritage artifacts. In: SPIE Defense+ Security, p. 101981A. International Society for Optics and Photonics (2017)
Bai, D., Messinger, D.W., Howell, D.: Pigment diversity estimation for hyperspectral images of the Selden Map of China. In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, vol. 10644, p. 1064415. International Society for Optics and Photonics (2018)
Bai, D., Messinger, D.W., Howell, D.: A hyperspectral imaging spectral unmixing and classification approach to pigment mapping in the Gough and Selden Maps. J. Am. Inst. Conserv. (2019). https://doi.org/10.1080/01971360.2019.1574436
Casini, A., Lotti, F., Picollo, M., Stefani, L., Buzzegoli, E.: Image spectroscopy mapping technique for noninvasive analysis of paintings. Stud. Conserv. 44(1), 39–48 (1999)
Delaney, J.K., Thoury, M., Zeibel, J.G., Ricciardi, P., Morales, K.M., Dooley, K.A.: Visible and infrared imaging spectroscopy of paintings and improved reflectography. Heritage Sci. 4(1), 6 (2016)
Delano-Smith, C., et al.: New light on the Medieval Gough Map of Britain. Imago Mundi 69(1), 1–36 (2016)
Devaram, R.R., Allegra, D., Gallo, G., Stanco, F.: Hyperspectral image classification via convolutional neural network based on dilation layers. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11751, pp. 378–387. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30642-7_34
Fischer, C., Kakoulli, I.: Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Stud. Conserv. 51(sup1), 3–16 (2006)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)
Li, Y., Zhang, H., Shen, Q.: Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9(1), 67 (2017)
Lilley, K.D., Lloyd, C.D., Campbell, B.M.S.: Mapping the realm: a new look at the Gough map of Britain cartographic veracity in medieval mapping: analyzing geographical variation in the Gough map of great Britain. Imago Mundi 61(1), 1–28 (2009)
Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959–4962. IEEE (2015)
Melessanaki, K., Papadakis, V., Balas, C., Anglos, D.: Laser induced breakdown spectroscopy and hyper-spectral imaging analysis of pigments on an illuminated manuscript. Spectrochim. Acta Part B Atomic Spectrosc. 56(12), 2337–2346 (2001)
Meng, Z., Li, L., Jiao, L., Feng, Z., Tang, X., Liang, M.: Fully dense multiscale fusion network for hyperspectral image classification. Remote Sens. 11(22), 2718 (2019)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Solopova, E.: The making and re-making of the Gough map of Britain: manuscript evidence and historical context. Imago Mundi 64(2), 155–168 (2012)
Wang, W., Dou, S., Jiang, Z., Sun, L.: A fast dense spectral-spatial convolution network framework for hyperspectral images classification. Remote Sens. 10(7), 1068 (2018)
Yue, J., Zhao, W., Mao, S., Liu, H.: Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sens. Lett. 6(6), 468–477 (2015)
Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847–858 (2017)
Zhong, Z., Li, J., Ma, L., Jiang, H., Zhao, H.: Deep residual networks for hyperspectral image classification. Institute of Electrical and Electronics Engineers (2017)
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Bai, D., Messinger, D.W., Howell, D. (2021). Deep Learning Spatial-Spectral Processing of Hyperspectral Images for Pigment Mapping of Cultural Heritage Artifacts. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12667. Springer, Cham. https://doi.org/10.1007/978-3-030-68787-8_14
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