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Discovering the Ancient Tomb under the Forest Using Machine Learning with Timing-Series Features of Sentinel Images: Taking Baling Mountain in Jingzhou as an Example

Yichuan Liu,Qingwu Hu,Shaohua Wang,Fengli Zou,M. Ai,P. Zhao

Published 2023 in Remote Sensing

ABSTRACT

Cultural traces under forests are one of the main problems affecting the identification of archaeological sites in densely forested areas, so it is full of challenges to discover ancient tombs buried under dense vegetation. The covered ancient tombs can be identified by studying the time-series features of the vegetation covering the ancient tombs on the multi-time series remote sensing images because the ancient tombs buried deep underground have long-term underground space structures, which affect the intrinsic properties of the surface soil so that the growth status of the covering vegetation is different from that of the vegetation in the area without ancient tombs. We first use the highly detailed DSM data to select the ancient tombs that cannot be visually distinguished on the optical images. Then, we explored and constructed the temporal features of the ancient tombs under the forest and the non-ancient tombs in the images, such as the radar timing-series features of Sentinel 1 and the multi-spectral and vegetation index timing-series features of Sentinel 2. Finally, based on these features and machine learning, we designed an automatic identification algorithm for ancient tombs under the forest. The method has been validated in Baling Mountain in Jingzhou, China. It is very feasible to automatically identify ancient tombs covered by surface vegetation by using the timing-series features of remote sensing images. Additionally, the identification of large ancient tombs or concentrated ancient tombs is more accurate, and the accuracy is improved after adding radar features. The paper concludes with a discussion of the current limitations and future directions of the method.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Remote Sensing

  • Publication date

    2023-01-17

  • Fields of study

    Computer Science, Environmental Science, History

  • Identifiers
    DOI 10.3390/rs15030554
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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