气候变化领域集成服务门户(CCPortal): Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods

CCPortal

DOI	10.1016/j.rse.2020.112129
	Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods
	Lorenz S.; Ghamisi P.; Kirsch M.; Jackisch R.; Rasti B.; Gloaguen R.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	Hyperspectral (HS) imaging holds great potential for the mapping of geological targets. Innovative acquisition modes such as drone-borne or terrestrial remote sensing open up new scales and angles of observation, which allow to analyze small-scale, vertical, or difficult-to-access outcrops. A variety of available sensors operating in different spectral ranges can provide information about the abundance and spatial location of various geologic materials. However geological outcrops are inherently uneven and spectrally heterogeneous, may be covered by dust, lichen or weathering crusts, or contain spectrally indistinct objects, which is why classifications or domain mapping approaches are often used in geoscientific and mineral exploration applications as a means to discriminate mineral associations (e.g. ore or alteration zones) based on overall variations in HS data. Feature extraction (FE) algorithms are prominently used as a preparatory step to identify the first order variations within the data and, simultaneously, reduce noise and data dimensionality. The most established FE algorithms in geosciences are, by far, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF). Major progress has been conducted in the image processing community within the last decades, yielding innovative FE methods that incorporate spatial information for smoother and more accurate classification results. In this paper, we test the applicability of conventional (PCA, MNF) and innovative FE techniques (OTVCA: Orthogonal total variation component analysis and WSRRR: Wavelet-based sparse reduced-rank regression) on three case studies from geological HS mapping campaigns, including drone-borne mineral exploration, terrestrial paleoseismic outcrop scanning and thermal HS lithological mapping. This allows us to explore the performance of different FE approaches on complex geological data with sparse or partly inaccurate validation data. For all case studies, we demonstrate advantages of innovative FE algorithms in terms of classification accuracy and geological interpretability. We promote the use of advanced image processing methods for applications in geoscience and mineral exploration as a tool to support geological mapping activities. © 2020 Elsevier Inc.
英文关键词	Classification; Domain mapping; Feature extraction; Hyperspectral imaging; Image processing; Mineral exploration
语种	英语
scopus关键词	Classification (of information); Drones; Extraction; Feature extraction; Hyperspectral imaging; Lithology; Mapping; Ores; Processing; Remote sensing; Classification accuracy; Classification results; Data dimensionality; Image processing - methods; Mineral associations; Minimum noise fraction; Reduced rank regression; Spatial informations; Mineral exploration; accuracy assessment; algorithm; geological mapping; image processing; mapping method; observational method; remote sensing; testing method
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179072
作者单位	Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Straße 40, Freiberg, 09599, Germany
推荐引用方式 GB/T 7714	Lorenz S.,Ghamisi P.,Kirsch M.,et al. Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods[J],2021,252.
APA	Lorenz S.,Ghamisi P.,Kirsch M.,Jackisch R.,Rasti B.,&Gloaguen R..(2021).Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods.Remote Sensing of Environment,252.
MLA	Lorenz S.,et al."Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods".Remote Sensing of Environment 252(2021).