地质通报2025，Vol.44Issue(7)：1187-1200,14.DOI:10.12097/gbc.2023.11.047
基于机器学习的新疆东天山黄山地区遥感岩性自动分类及其识别精度分析
Automatic classification of remote sensing lithology in the Huangshan Area of the Eastern Tianshan Mountains in Xinjiang Based on machine learning and analysis of its recognition accuracy
摘要
Abstract
[Objective]Remote sensing lithology mapping is of great significance for both basic geological research and mineral exploration.Aiming at the problems of low efficiency and strong subjectivity of traditional lithology interpretation methods in complex bedrock areas,this study takes the Huangshan area of the Eastern Tianshan Mountains in Xinjiang as the research area,aiming to construct an automatic classification model integrating spectroscopic and spatial characteristics.Improve the lithology identification accuracy of ASTER data in bedrock exposure areas and provide technical support for mineral resource exploration.[Methods]Propose a collaborative framework of watershed segmentation and regularized extreme learning machine:①Extract spatial boundary features through the watershed algorithm and establish a spatial constraint rule base;②Principal component analysis and L2 regularization are adopted to optimize the spectral feature space and simplify the hidden layer structure of ELM.③Design the maximum voting mechanism to integrate spectral classification and spatial constraint results.And compare and verify the model performance with four traditional algorithms such as Support Vector Machine(SVM),maximum likelihood method,and Markov distance method.[Results]The experiments show that:①The overall accuracy of the fusion model reaches 92.13%(Kappa=0.91),which is significantly improved compared with traditional classification methods such as SVM;②Spatial characteristics improve the discrimination accuracy of similar rock types such as granite;③After feature dimension reduction,the model parameters were significantly reduced and the classification time was greatly shortened.[Conclusions]This model effectively breaks through the bottleneck of single spectral classification through multi-feature fusion,providing a new lithology identification scheme with high precision and high efficiency for the bedrock area.It can be adapted to data such as WorldView-3 and extended to similar bedrock exposure areas.关键词
岩性分类/机器学习/多光谱遥感/极限学习机/空间特征/新疆东天山Key words
lithologic classification/machine learning/multi-spectral remote sensing/extreme learning machine/spatial feature/eastern Tianshan Mountains in Xinjiang分类
天文与地球科学引用本文复制引用
刘磊,王乐,张凯南,梅佳成,张群佳..基于机器学习的新疆东天山黄山地区遥感岩性自动分类及其识别精度分析[J].地质通报,2025,44(7):1187-1200,14.基金项目
陕西省自然科学基础研究计划项目《鄂尔多斯盆地南缘碳酸盐黏土型锂矿遥感探测机理及锂元素定量反演》(编号:2023-JC-ZD-18)、自然资源部黄河上游战略性矿产资源重点实验室开放课题资助项目《甘肃北山镁铁—超镁铁小岩体型矿化定量遥感探测研究》(编号:YSMRKF202203) Supported by Shaanxi Provincial Natural Science Basic Research Program Project"Remote Sensing Detection Mechanism of Carbonate Clay-type Lithium Deposits in the Southern Margin of the Ordos Basin and Quantitative Inversion of Lithium Elements"(No.2023-JC-ZD-18),the Open Project of the Key Laboratory of Strategic Mineral Resources in the Upper Reaches of the Yellow River,Ministry of Natural Resources,"Quantitative Remote Sensing Detection Research on Mineralization of Magnesium-Iron-Super Magnesium-Iron Small Rock Type in Beishan,Gansu Province"(No.YSMRKF202203) （编号:2023-JC-ZD-18）
