Geomatics Engineering, Department of Civil Engineering, Indian Institute of Technology, (IIT), Roorkee, 247667, India.
Environ Monit Assess. 2023 Apr 15;195(5):575. doi: 10.1007/s10661-023-11200-1.
Remote sensing datasets and methods are suitable for mapping and managing the natural resources like minerals, clean water, and energy and also govern their sustainability nowadays. Hyperspectral (HS) imaging has immense potential for rock type classification, mineral mapping, and identification. This work demonstrates the potential of feature extraction techniques and unsupervised machine learning methods for the space-borne hyperspectral remote sensing data in characterizing and identifying mineral and classifying rock type in Banswara, Rajasthan, India. Feature extraction techniques can reveal variations within the data, which can help identify geological areas, reduce noise, and check the dimensionality of the data. Singular value decomposition (SVD)-based principal component analysis (PCA), kernel PCA (KPCA), minimum noise fraction (MNF), and independent component analysis (ICA) were tested for lithological mapping using recently launched DLR Earth Sensing Imaging Spectrometer Hyperspectral (DESIS) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) data in order to map geologically significant areas. Unsupervised machine learning methods, such as Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means, were also employed. Vertex component analysis (VCA) was utilized to check for similarity and identify various spectral features. Our work demonstrates the advantages of using feature extraction algorithms such as PCA and KPCA over MNF and ICA in geological mapping and interpretability. We recommend K-means as the preferred method for lithological classification of hyperspectral remote sensing data. Our work highlights the potential of advanced feature extraction algorithms for mineral mapping using hyperspectral data, providing different ways to identify minerals and ultimately leading to better mineral resource management.
遥感数据集和方法适用于当前的矿产、清洁水和能源等自然资源的测绘和管理，以及它们的可持续性管理。高光谱 (HS) 成像在岩石类型分类、矿物制图和识别方面具有巨大潜力。这项工作展示了特征提取技术和无监督机器学习方法在星载高光谱遥感数据中的应用潜力，用于表征和识别矿物以及对印度拉贾斯坦邦班斯瓦拉的岩石类型进行分类。特征提取技术可以揭示数据内部的变化，这有助于识别地质区域、减少噪声并检查数据的维度。奇异值分解 (SVD) 主成分分析 (PCA)、核主成分分析 (KPCA)、最小噪声分数 (MNF) 和独立成分分析 (ICA) 被用于对最近发射的 DLR 地球感应成像光谱仪高光谱 (DESIS) 和 PRISMA 数据进行岩性制图，以绘制地质意义重大的区域。还使用了无监督机器学习方法，如迭代自组织数据分析技术 (ISODATA) 和 K-均值。顶点成分分析 (VCA) 用于检查相似性并识别各种光谱特征。我们的工作表明，在地质制图和可解释性方面，使用 PCA 和 KPCA 等特征提取算法比 MNF 和 ICA 具有优势。我们推荐 K-均值作为高光谱遥感数据岩性分类的首选方法。我们的工作强调了使用高级特征提取算法进行高光谱数据矿物制图的潜力，为识别矿物提供了不同的方法，最终有助于更好地管理矿物资源。
