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

作　　者：叶叶 YE Ye(Cloud Computing Big Data Laboratory of School of Information Engineering,Taizhou University,Taizhou 225300,China)

机构地区：[1]泰州学院信息工程学院云计算大数据实验室,江苏泰州225300

出　　处：《红外技术》2025年第4期453-458,共6页Infrared Technology

基　　金：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)。

摘　　要：高光谱图像和深度学习技术为土壤污染物检测提供了新的方法和工具。本研究旨在探索基于卷积神经网络(CNN)的高光谱土壤污染物检测算法。首先,收集了包含多个波段的高光谱土壤数据集,并进行数据分析和特征提取;然后,设计了一种适应高光谱土壤数据特点的CNN网络架构,提出针对高光谱数据特点的自注意力机制,自动对冗余光谱数据降维,再使用图谱特征融合特征提取结构构建模型;最后,在收集的土壤污染物数据集上验证算法性能。实验结果表明,所提出的方法能够对高光谱数据有效降维,降低数据冗余程度,通过融合图谱特征,在土壤污染物检测方面取得了较好的性能和准确性,对土壤污染物的快速检测有一定实际意义。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 

分 类 号：TP751.2[自动化与计算机技术—检测技术与自动化装置]