世界核地质科学2026，Vol.43Issue(3)：624-636,13.DOI:10.3969/j.issn.1672-0636.2026.03.018
基于多种机器学习方法的SASI航空高光谱数据蚀变矿物填图研究
Alteration mineral mapping from airborne hyperspectral SASI data using multiple machine learning approaches
摘要
Abstract
Alteration mineral mapping is an important application of hyperspectral remote sensing in geological exploration.Rapid and accurate completion of alteration mapping for ultra large volume hyperspectral datasets using machine learning models currently presents a major technical challenge.This study addresses the issues of insufficient reliability in supervised classification sample labels and unclear performance differences among various models and uses SASI airborne hyperspectral data from the Liuyuan area to develop a semi-automatic mineral labeling approach based on expert knowledge,and conducts a comparative analysis of multiple machine learning methods.Candidate mineral spectra are first derived through endmember extraction and matching with a standard spectral library,and the initial mapping results are further refined using mixture tuned matched filtering in combination with field verification data,yielding a relatively reliable mineral distribution dataset.Based on this dataset,several classifiers,including support vector machine(SVM),Random Forest,Decision Tree,Gradient Boosting,AdaBoost,Multi-layer Perceptron(MLP),and a Three-dimensional Convolutional Neural-network(3D-CNN),are evaluated using overall accuracy(OA),average accuracy(AA),and the Kappa coefficient.The results show that SVM achieves the best overall performance(OA=96.5％,Kappa=0.956),while AdaBoost performs comparatively poorly.Medium-Al muscovite is best identified by the gradient boosting model,whereas epidote exhibits notable confusion across all classifiers.The 3D-CNN achieves an OA of 95.89％,slightly lower than that of SVM,but shows improved class balance as reflected by higher AA.Overall,the results indicate that classification performance is largely governed by spectral separability,while the incorporation of spatial information contributes to improved class balance but does not consistently enhance overall accuracy.These findings provide useful insights for sample construction and model selection in hyperspectral alteration mineral mapping.关键词
蚀变矿物填图/SASI影像/航空高光谱遥感/机器学习/3D-CNN网络Key words
alteration mineral mapping/SASI data/airborne hyperspectral imaging/machine learning/3D convolutional neural network分类
信息技术与安全科学引用本文复制引用
杨惠麟,赵英俊,秦凯,郝予希,李明,朱玲,杨越超,李凌昊,王希民..基于多种机器学习方法的SASI航空高光谱数据蚀变矿物填图研究[J].世界核地质科学,2026,43(3):624-636,13.基金项目
中核集团研发平台稳定支持科研项目(编号:遥YFPT2301)资助 Supported by Stable Support Research Project of CNNC R&D Platforms(No.遥YFPT2301) （编号:遥YFPT2301）
