Jianxin Sun, Xinyan Xie, Yu Pan, Yakub Islamov, Yufeng Ge, Hongfeng Yu

Abstract

Soil measurement and evaluation are crucial to various aspects of agriculture, including agricultural productivity, nutrient management, water management, and pH Regulation. Hyperspectral imaging is an advanced technique used to capture and analyze a wide range of light wavelengths (or spectral bands) across the electromagnetic spectrum. Hyperspectral imaging in soil research involves the use of this advanced imaging technique to analyze the spectral properties of soils. It allows researchers to capture detailed information about the composition, texture, and conditions of soil across a wide range of wavelengths in the electromagnetic spectrum. This in-depth spectral analysis provides valuable insights for studying soil health, nutrient content, moisture levels, and other critical parameters. However, existing hyperspectral analysis of soil relies on using imaging systems to exclusively capture information from the soil surface. This yields a two-dimensional image in which each pixel represents a spectrum vector. In this paper, we provide a new 3D hyperspectral data capturing features deep into the soil where each voxel represents a spectrum vector. For effective analysis of this type of new hyperspectral data, we develop a 3D visualization tool to not only directly visualize individual spectrum of the soil volume but also provide a way to cluster such high dimensional data leveraging a deep learning-based method through autoencoder.

Bibtex

@INPROCEEDINGS{10386924,
  author={Sun, Jianxin and Xie, Xinyan and Pan, Yu and Islamov, Yakub and Ge, Yufeng and Yu, Hongfeng},
  booktitle={2023 IEEE International Conference on Big Data (BigData)}, 
  title={Visualization of 3D Hyperspectral Soil Mapping Data via Autoencoder-based Clustering}, 
  year={2023},
  volume={},
  number={},
  pages={3503-3509},
  keywords={Three-dimensional displays;Surface waves;Soil measurements;Pipelines;Data visualization;Imaging;Soil;hyperspectral;3D visualization;soil;deep learning;clustering},
  doi={10.1109/BigData59044.2023.10386924}
}