Abstract：
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.
摘要：
遥感数据集和方法适用于绘制和管理自然资源，如矿物，干净的水,和能源，也控制着它们的可持续性。高光谱（HS）成像对岩石类型分类具有巨大的潜力，矿物测绘,和识别。这项工作证明了空间高光谱遥感数据的特征提取技术和无监督机器学习方法在Banswara中表征和识别矿物和分类岩石类型方面的潜力，拉贾斯坦邦,印度。特征提取技术可以揭示数据中的变化，这可以帮助识别地质区域，减少噪音,并检查数据的维度。基于奇异值分解(SVD)的主成分分析(PCA)，内核PCA(KPCA)，最小噪声分数(MNF)，使用最近推出的DLR地球传感成像光谱仪高光谱（DESIS）和PRecursoreIperSpettraledellaMissioneApplicativa（PRISMA）数据对岩性制图进行了测试，以绘制具有地质意义的区域。无监督机器学习方法，如迭代自组织数据分析技术(ISODATA)和K-means，也被雇用。顶点成分分析（VCA）用于检查相似性并识别各种光谱特征。我们的工作证明了在地质制图和可解释性中使用PCA和KPCA等特征提取算法相对于MNF和ICA的优势。我们建议K-means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光谱数据进行矿物测绘的高级特征提取算法的潜力，提供不同的方法来识别矿物，并最终导致更好的矿物资源管理。
