Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

Abstract

Hyperspectral imaging has been used in a variety of geological applications since its advent in the 1970s. In the last few decades, different techniques have been developed by geologists to analyze hyperspectral data in order to quantitatively extract geological information from the high-spectral-resolution remote sensing images. We attempt to review and update various steps of the techniques used in geological information extraction, such as lithological and mineralogical mapping, ore exploration, and environmental geology. The steps include atmospheric correction, dimensionality processing, endmember extraction, and image classification. It is identified that per-pixel and subpixel image classifiers can generate accurate alteration mineral maps. Producing geological maps of different surface materials including minerals and rocks is one of the most important geological applications. The hyperspectral images classification methods demonstrate the potential for being used as a main tool in the mining industry and environmental geology. To exemplify the potential, we also include a few case studies of different geological applications.

Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

“…An initial concern in this study was the fact that the used imaging hyperspectral systems were not covering the spectral range 2000-2500 nm, which is diagnostic for many geological materials (see, e.g., [4,9]). Imaging systems covering a wider spectral range have been used to acquire panoramic images in open pit mines [21][22][23][24]29,[74][75][76].…”

“…Most hyperspectral images have been remotely acquired from airborne sensors and a few satellites and, in the context of the mining industry, common applications of remotelysensed hyperspectral images are mineral exploration (see examples in [8][9][10][11][12]) and the environmental impact [13][14][15]. Currently, close-range hyperspectral images of hand samples and/or drill cores [16][17][18][19], along with ground-based panoramic hyperspectral imaging of semi-vertical outcrops [20][21][22][23][24], are increasingly used as hyperspectral imaging systems become more portable and widespread.…”

A 3-stage Spectral-spatial Method for Hyperspectral Image Classification

“…In general, each distinct material has its own spectral signature in HSIs owing to its unique chemical composition. The improvement in spectral resolution makes it possible to explore the HSIs using machine learning approaches in various applications like land coverage mapping, change recognition, water quality monitoring, and mineral identification [2]- [5]. The rich information in HSIs enables the algorithms to distinguish more detailed categories for land cover clustering and classification so that HSIs play a vital role in detecting different natural resources and monitoring vegetation health [6]- [16].…”

Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

“…Much of the interest in hyperspectral imaging for geological applications has been in the field of remote sensing, as summarised by van der Meer et al [ 5 ] and, more recently, Peyghambari and Zhang [ 6 ]. Dedicated hyperspectral sensing platforms and facilities such as airborne sensors (e.g., AVIRIS [ 7 ]) and spaceborne sensors (e.g., Hyperion [ 8 ] and EnMAP [ 9 ]) provide a wealth of data for researchers.…”