Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 411-416, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-411-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 411-416, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-411-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

M. Gu1, S. Lyu1,2, M. Hou1,2, S. Ma3, Z. Gao3, S. Bai3, and P. Zhou4 M. Gu et al.
  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, No.15 Yongyuan Road, Daxing District, Beijing, 102616, China
  • 2Beijing Key Laboratory For Architectural Heritage Fine Reconstruction & Health Monitoring, No.15Yongyuan Road, Daxing District, Beijing, 102616, China
  • 3Shanxi Provincial Institute of Archeology, Taiyuan, Shanxi Province, 030001, China
  • 4China university of mining and technology, No. 11,Xueyuan Road D, Haidian District, Beijing, 100083, China

Keywords: Hyperspectral imaging technology, PCA, Feature bands selection, SVM, Identification of matter

Abstract. There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.