SpectralNet-based soil structure characterization

Zhilin Li; Yifeng Yong; Yahui Shen

doi:10.1117/12.3055636

12 December 2024 SpectralNet-based soil structure characterization

Zhilin Li, Yifeng Yong, Yahui Shen

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 1343932 (2024) https://doi.org/10.1117/12.3055636
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

This study explores the application of SpectralNet in soil structural characterization based on the SpectralNet method, aiming at the effective identification and clustering of soil samples by this algorithm. SpectralNet is a novel method that combines deep learning and spectral clustering and achieves the clustering objective by learning a low-dimensional representation of the data. In this paper, we first introduce the preprocessing process of soil sample data, then describe in detail the working principle of the SpectralNet algorithm and its application in soil clustering, and analyze the parameter sensitivity of SpectralNet in detail. The experimental results show that compared with traditional clustering methods, SpectralNet-based soil clustering can effectively process soil data and improve the interpretability of clustering results while maintaining high clustering accuracy. This study provides a new data analysis tool for the field of soil science and provides reference and inspiration for future research and application in related fields.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhilin Li, Yifeng Yong, and Yahui Shen "SpectralNet-based soil structure characterization", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 1343932 (12 December 2024); https://doi.org/10.1117/12.3055636

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