A Review of Hyperspectral Imaging in Close Range Applications

Abstract

ABSTRACT:Hyperspectral imaging is an established method for material mapping, which has been conventionally applied from airborne and spaceborne platforms for a range of applications, including mineral and vegetation mapping, change detection and environmental studies. The main advantage of lightweight hyperspectral imagers lies in the flexibility to deploy them from various platforms (terrestrial imaging and from unmanned aerial vehicles; UAVs), as well as the high spectral resolution to cover an expanding wavelength range. In addition, spatial resolution allows object sampling distances from micrometres to tens of centimetrescomplementary to conventional nadir-looking systems. When this new type of imaging device was initially released, few instruments were available and the applicability and potential of the method was restricted. Today, a wider range of instruments, with a range of specifications, is available, with significant improvements over the first generation of technology. In this contribution, the state-ofthe-art of hyperspectral imaging will be reviewed from a close range measurement perspective, highlighting how the method supplements geometric modelling techniques. An overview of the processing workflow, adjusted to the more complex close range imaging scenario will be given. This includes the integration with 3D laser scanning and photogrammetric models to provide a geometric framework and real world coordinate system for the hyperspectral imagery.

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

“…Hyperspectral imaging is also used in practice outside the realm of earth or geosciences for example in art to study paintings (Capobianco et al, 2015;da Silva et al, 2015), the pharmaceutical industry uses it for quality control (da Silva et al, 2015;Mainali et al, 2014) and hyperspectral scanners are used for medical imaging (Calin et al, 2014;Lu and Fei, 2014). In earth sciences, hyperspectral imaging is conducted at various scales varying from rock samples (Cooper et al, 2002;Green and Schodlok, 2016) through core scanning (Bolin and Moon, 2003;Mathieu et al, 2017;Tappert et al, 2015) and outcrop scanning (Buckley et al, 2013;Kurz and Buckley, 2016;Murphy et al, 2012;Snyder et al, 2016) to airborne and space borne acquisition (van der Meer et al, 2012). There are several airborne hyperspectral imaging systems that are operated on a commercial basis and recently also drones have been used for geologic remote sensing (Jakob et al, 2017).…”

Using hyperspectral unmixing for the analysis of very high spatial resolution hyperspectral imagery

“…Unmixing is a valuable analysis technique to analyze satellite and airborne hyperspectral imagery of low to moderate spatial resolution. As imagers become more affordable, very high spatial resolution hyperspectral imaging is used in laboratory, computer vision, standoff, close-range and UxS remote sensing applications [1,2,3]. An interesting question we are trying to address in this work is if hyperspectral image processing techniques designed for moderate to low-resolution imagery can still be of value in very high spatial resolution hyperspectral imaging (VHSR-HSI).…”

Integration of Terrestrial and Drone-Borne Hyperspectral and Photogrammetric Sensing Methods for Exploration Mapping and Mining Monitoring

“…Hyperspectral imagery has recently been integrated into digital outcrops (e.g., [20][21][22][23][24][25]), but using only hyperspectral data in the visible to near infrared (VNIR) and the shortwave infrared (SWIR) part of the electromagnetic spectrum, which lacks distinctive Si-O bond-related spectral features [26]. Hyperspectral long-wave infrared (LWIR) imaging complements VNIR-SWIR data in the field of mineral mapping, since the molecular vibrations of many rock-forming minerals have characteristic resonant wavelengths in the LWIR part of the electromagnetic spectrum [27].…”

“…Digital outcrop models are traditionally based on data obtained from laser scanning (e.g., [4,[45][46][47]) or photogrammetric techniques (e.g., [48][49][50][51][52]). Fusion between hyperspectral and 3D outcrop data is mostly based on terrestrial laser scanning (TLS) data (e.g., [20][21][22][23]53,54]). TLS can be used to derive highly precise outcrop models (e.g., [48,[55][56][57]), but these are prone to containing data gaps caused by occlusion, particular in areas of high relief.…”