红外技术2025，Vol.47Issue(4)：453-458,6.
基于注意力机制的土壤重金属污染物高光谱检测深度学习方法
A Deep Learning Method for Hyperspectral Detection of Heavy Metal Contaminants in Soil Based on Attention Mechanism
摘要
Abstract
Hyperspectral imaging and deep learning techniques provide new methods and tools for detecting soil contaminants.This study explores a convolutional neural network(CNN)-based algorithm for the detection of hyperspectral soil contaminants.First,a hyperspectral soil dataset containing multiple spectral bands was collected,and data analysis and feature extraction were performed.Subsequently,a CNN architecture adapted to the characteristics of hyperspectral soil data was designed.A self-attention mechanism was introduced to automatically reduce the dimensionality of redundant spectral data,and a feature fusion structure using graph features was employed for feature extraction.Finally,the performance of the algorithm was validated using a collected soil contaminant dataset.The experimental results demonstrate that the proposed method effectively reduces the dimensionality of hyperspectral data,decreases data redundancy,and achieves good performance and accuracy in soil contaminant detection by incorporating graph features.This method is of practical significance for the rapid detection of soil contaminants.关键词
高光谱/土壤污染/注意力机制Key words
hyperspectral/soil contamination/attention mechanism分类
计算机与自动化引用本文复制引用
叶叶..基于注意力机制的土壤重金属污染物高光谱检测深度学习方法[J].红外技术,2025,47(4):453-458,6.基金项目
江苏省高校哲学社会科学研究项目(2022SJYB2329),泰州学院2021年度教育教学改革研究课题(2021JGB05),泰州市软科学研究计划项目(RKX20210024). （2022SJYB2329）
