Open Access

ARTICLE

Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion

by Shi Qiu¹, Pengchang Zhang^1,*, Xingjia Tang², Zimu Zeng¹, Miao Zhang¹, Bingliang Hu^1,*

1 Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
2 Institute of Culture and Heritage, Northwest Polytechnic University, Xi’an, China

* Corresponding Authors: Pengchang Zhang. Email: ; Bingliang Hu. Email:

(This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)

Computers, Materials & Continua 2023, 77(3), 3783-3800. https://doi.org/10.32604/cmc.2023.042074

Received 17 May 2023; Accepted 09 October 2023; Issue published 26 December 2023

Abstract

Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history, science, culture, art and research. However, mainstream analytical methods are contacting and detrimental, which is unfavorable to the protection of cultural relics. This paper improves the accuracy of the extraction, location, and analysis of artifacts using hyperspectral methods. To improve the accuracy of cultural relic mining, positioning, and analysis, the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques. Firstly, region stitching algorithm based on the relative position of hyper spectrally collected data is proposed to improve stitching efficiency. Secondly, given the prominence of traditional HRNet (High-Resolution Net) models in high-resolution data processing, the spatial attention mechanism is put forward to obtain spatial dimension information. Thirdly, in view of the prominence of 3D networks in spectral information acquisition, the pyramid 3D residual network model is proposed to obtain internal spectral dimensional information. Fourthly, four kinds of fusion methods at the level of data and decision are presented to achieve cultural relic labeling. As shown by the experiment results, the proposed network adopts an integrated method of data-level and decision-level, which achieves the optimal average accuracy of identification 0.84, realizes shallow coverage of cultural relics labeling, and effectively supports the mining and protection of cultural relics.

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Keywords

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