Research progress on feature band extraction for hyperspectral inversion of soil composition

LIANG Yonggang; YANG Jinyan

Chinese

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Research progress on feature band extraction for hyperspectral inversion of soil composition

Survey & Review | 更新时间：2025-12-30

- Research progress on feature band extraction for hyperspectral inversion of soil composition
- Vol. 44, Issue 11, Pages: 7-11(2025)
- 作者机构：
  
  四川大学建筑与环境学院,四川,成都,610000
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391;X833
- Published：2025
- 稿件说明：
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LIANG Yonggang, YANG Jinyan. Research progress on feature band extraction for hyperspectral inversion of soil composition[J]. 2025, 44(11): 7-11.
DOI：

LIANG Yonggang, YANG Jinyan. Research progress on feature band extraction for hyperspectral inversion of soil composition[J]. 2025, 44(11): 7-11. DOI：

摘要

高光谱技术凭借其高分辨率和无损检测的优势，成为土壤成分分析的有力工具。然而，高光谱数据的高维性和冗余性增加了数据处理的复杂度，特征波段提取成为关键问题。本文系统梳理了基于光谱特征和基于数据驱动的特征波段提取方法，分析了7种计算方法的优势与不足。通过比较不同方法在土壤成分反演中的应用效果，指出了当前研究面临的挑战，主要包括特征波段提取方法的优化不足、数据驱动方法的物理可解释性欠缺、高光谱数据处理效率与模型适应性提升的需求，并展望了未来的发展方向，结合光谱特征与数据驱动的方法，有望提升土壤成分反演的精度和稳定性。

Abstract

Hyperspectral technology is considered a powerful tool for soil composition analysis due to its advantages of high resolution and non-destructive detection.However

the high dimensionality and redundancy of hyperspectral data increase the complexity of data processing

making feature band extraction a critical issue.This paper systematically reviews feature band extraction methods based on spectral characteristics and data-driven approaches

and analyzes the strengths and limitations of seven computational methods.Through comparative analysis of different methods’application effectiveness in soil composition inversion

several current research challenges are identified:insufficient optimization of feature band extraction methods

lack of physical interpretability in data-driven approaches

and the need for improved processing efficiency and model adaptability in hyperspectral data handling.Future development directions are proposed

suggesting that the integration of spectral feature-based and data-driven methods could potentially enhance the accuracy and stability of soil composition inversion.

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