Hyperspectral Shortwave Infrared Research Articles

Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques

Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares–discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans.

Aerial detection of beached marine plastic using a novel, hyperspectral short-wave infrared (SWIR) camera

Abstract Plastic pollution in the marine environment is a pervasive, global problem that threatens wildlife and human health. Routine monitoring is required to determine pollution hotspots, focus clean-up efforts, and assess the efficacy of legislation implemented to reduce environmental contamination. The shoreline represents an accessible area, relative to open water, from which to monitor this. Unmanned aerial vehicles (UAVs) offer a low-cost platform for remote sensing that operates below cloud coverage, which can interfere with satellite imagery. Detection of plastic using visible light is limited however, and results may be improved by using short-wave infrared (SWIR) imagery to collect chemical information. Within the commercial recycling industry, plastic items are sorted successfully based on their composition using SWIR instrumentation that measures the chemical spectra of waste items under controlled illumination. Here, proof of concept is established for aerial detection of domestic and shoreline-harvested plastic items on a beach under natural sunlight with a lightweight (800 g), hyperspectral SWIR camera deployed at an altitude of ∼ 5 m over ∼ 30-m transects. The results of spectral correlation mapping to compare imagery spectra to polyethylene and polypropylene reference spectra demonstrate that these two polymers can be successfully detected with this novel method.

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.

Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork

Monitoring and maintaining the freshness of meat is important to ensuring a supply of meat that is safe for consumption. The objective of this study is to present a shortwave infrared (SWIR) hyperspectral imaging system in combination with partial least-squares regression (PLSR) model and feature selection methods that can be used for the prediction of the total volatile basic nitrogen (TVB-N) content in fresh pork. The SWIR hyperspectral reflectance images were acquired for pork samples removed from refrigerated storage after 1, 4, 8, 11, 15, and 21 days. The hyperspectral SWIR images and actual TVB-N contents were used for constructing the PLSR model. PLSR models were optimized by using feature selection strategies such as random frog (RF) and variable importance in projection (VIP) score. The predictions from the optimal RF-PLSR model value with maximum normalization preprocessing exhibited correlation coefficient values for Rc2 and Rp2 of 0.94 and 0.90, respectively. Moreover, this research showed that visualization of TVB-N levels applied to the optimal model based on selected wavebands provide an intuitive way to interpret the spatial information of the sample. This study revealed that the multivariate models developed here for rapid and nondestructive evaluation of pork freshness can be feasible for use in online inspection systems as an effective substitute for traditional methods to evaluate pork freshness.

Characterization of Mineralogy in the Highland Valley Porphyry Cu District Using Hyperspectral Imaging, and Potential Applications

The Highland Valley Copper (HVC) district in British Columbia, Canada, is host to at least four major porphyry Cu systems: Bethlehem (~209 Ma), and Valley, Lornex, and Highmont (~208 to 207 Ma). High spatial resolution (0.2–1.0 mm/pixel) hyperspectral imagery in the shortwave infrared (SWIR) were acquired on 755 rock samples and 400 m of continuous drill core. Spectral metrics are used to measure the relative abundance of 12 minerals and an additional metric is derived to estimate white mica grain size. In the Valley and Lornex deposits, coarse-grained white mica is associated with mineralization and is detectable up to 4 km away from the deposits. Kaolinite is present within 2 km of the mineralized centers but does not necessarily occur within strongly mineralized intervals. Prehnite is ubiquitous from 4 to 8 km from the deposits. In the Bethlehem deposit, tourmaline and epidote are associated with mineralization. We propose a spectral alteration score based on these proximal hyperspectral SWIR mineralogical patterns to assist explorers in targeting porphyry Cu systems when using drill core, surface rock samples and potentially remote sensing imagery. In a production environment, this metric could serve to facilitate ore-sorting.

A Novel Method to Remove Interference Fringes for Hyperspectral SWIR Imagers

A hyperspectral short-wave infrared (SWIR) imager with a HgCdTe focal plane detector usually suffers from interference fringes in spectral and spatial dimension due to the etalon effect. It can cause a modulation of sensitivity higher than 15% when the spectral resolution is higher than 10 nm. In this article, a two-step method is proposed and evaluated to remove interference fringes for hyperspectral SWIR imagers. First, we extract the spectral data, conduct harmonic decomposition to suppress the fringes in spectral dimension, and calculate the correction coefficients. Second, we optimize the correction coefficients according to the distribution of the interference fringes in spatial dimension. Sample SWIR images acquired from the Advanced Hyperspectral Imager (AHSI) on China’s GaoFen-5 satellite are used to evaluate the performance of the algorithm. It shows that the proposed method can well preserve the original spectral pattern and reduce the peak-to-peak amplitude of spatial fringing from ±15% to ±4%. The method is compared with three other previously proposed methods. It turns out that the newly proposed method has strong adaptability, high accuracy, and high efficiency.

Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.

Mineral footprints of the Paleoproterozoic sediment-hosted Abra Pb-Zn-Cu-Au deposit Capricorn Orogen, Western Australia

The minerals exploration undercover relies on the understanding of the ore-forming processes and the associated alteration characteristics that are used in vectoring towards a deposit. Previously, the proposed process for forming the 1590 Ma Abra base-metal deposit, hosted at 250 m depth in the Paleoproterozoic Edmund Basin of the Western Australian Capricorn Orogen, was through exhalation to the seafloor (SEDEX-style). Lacking any visible signs of alteration, sediments overlying the Abra deposit were perceived as younger, unaltered, and cutting the mineralisation and associated alteration comprised of siderite, chlorite and minor sericite. However, a recent study delineated a phengitic (i.e. Al-poor, Mg-, Fe-, Si-rich) white mica footprint in regolith above the deposit from hyperspectral field and remote sensing data. This study links the surficial mineral footprint to the deposit below through detailed analysis of hyperspectral data from drill core. The hyperspectral data processing results were validated using optical microscopy, micro-XRF and SEM-EDS analysis, and compared with geochemical, lithological and structural drill core data. Hyperspectral short-wave infrared (SWIR) data revealed a vertically zoned alteration footprint from the deposit to the surface, where very fine-crystalline phengitic white mica (i.e. sericite) and chlorite are most abundant a few tens of metres above the deposit. The abundance of chlorite gradually decreases, and the distal phengitic alteration is cut by the present erosional surface, but only moderately affected by weathering, confirming the surficial footprint detected in the previous study. Similarly, a vertically zoned carbonate alteration was delineated from thermal infrared (TIR) reflectance spectra from drill cores up to 2 km away from the deposit. Carbonate alteration from proximal to distal comprised of Mn-rich siderite, ankerite and ferroan dolomite, which replace the dolomite matrix in coarse-grained stratigraphic sequences. The replacement of dolomite matrix suggests that these permeable horizons acted as fluid conduits, which channelled hydrothermal fluids into sedimentary strata from a feeder structure. The presence of a vertically zoned carbonate and chlorite-phengite footprint in the overlying sediments advocates for their deposition before the ore formation at Abra, and a sub-seafloor replacement within a permeable sedimentary sequence as the ore-forming process. Comparable alteration footprints have been described around some world-class sediment-hosted base-metal deposits, including the Sullivan in the British Columbia, which is considered as a classic SEDEX-style by some and an exhalative-replacement hybrid by others. Also, identical footprints are common around the volcanic-hosted massive sulphide and are associated with the carbonate replacement-style deposits – an overlap which is an interesting future research space where hyperspectral technologies could provide answers. Validated hyperspectral data combined with geochemical, lithological and structural information enabled the identification of a subtle mineral footprint above the Abra deposit, and the established method can be used for vectoring towards other buried mineralisations – even through weathering.

Sensing Ocean Plastics with an Airborne Hyperspectral Shortwave Infrared Imager.

Here, we present a proof-of-concept on remote sensing of ocean plastics using airborne shortwave infrared (SWIR) imagery. We captured red, green, and blue (RGB) and hyperspectral SWIR imagery with equipment mounted on a C-130 aircraft surveying the "Great Pacific Garbage Patch" at a height of 400 m and a speed of 140 knots. We recorded the position, size, color, and type (container, float, ghost net, rope, and unknown) of every plastic piece identified in the RGB mosaics. We then selected the top 30 largest items within each of our plastic type categories (0.6-6.8 m in length) to investigate SWIR spectral information obtained with a SASI-600 imager (950-2450 nm). Our analyses revealed unique SWIR spectral features common to plastics. The SWIR spectra obtained ( N = 118 items) were quite similar both in magnitude and shape. Nonetheless, some spectral variability was observed, likely influenced by differences in the object optical properties, the level of water submersion, and an intervening atmosphere. Our simulations confirmed that the ∼1215 and ∼1732 nm absorption features have potential applications in detecting ocean plastics from spectral information. We explored the potential of SWIR remote sensing technology for detecting and quantifying ocean plastics, thus provide relevant information to those developing better monitoring solutions for ocean plastic pollution.

White Mica as a Hyperspectral Tool in Exploration for the Sunrise Dam and Kanowna Belle Gold Deposits, Western Australia

Patterns in white mica (muscovite, paragonite, and phengite) Al-Si chemistry (Tschermak substitution) were examined for the Archean Kanowna Belle and Sunrise Dam gold deposits in the Eastern Goldfields of Western Australia, using short-wave infrared (SWIR) spectrometry of drill core samples. Both of these deposits represent greenschist-facies, structurally controlled, >10-Moz gold systems with broadly similar host rocks and superimposed metasomatic hydrothermal alteration halos that extend for up to 3 km from the economic zone. However, zonation toward gold mineralization at Kanowna Belle is associated with increasingly longer wavelength white mica, whereas Sunrise Dam comprises the opposite pattern. The study also found that gold mineralization at Kanowna Belle is associated with quartz, chalcopyrite, and pyrite with negative δ 34 S values and a relative lack of carbonate, chlorite, and paragonite (Na-rich white mica), whereas gold mineralization at Sunrise Dam is associated with paragonite as well as carbonate, Fe-rich chlorite and pyrite with positive δ 34 S values and a relative lack of quartz and chalcopyrite. Thermodynamic numerical modeling revealed that these contrasting patterns in white mica Tschermak substitution (and associated alteration mineralogy) can be explained by two different types of ore fluids, namely (1) oxidized, alkaline, and silica-rich at Kanowna Belle; and (2) reduced, acid, Fe-rich and silica-poor at Sunrise Dam, though the sources of these contrasting fluids are less clear. Importantly for explorers, spectral measurement (using drill core, field, airborne, and potentially satellite hyperspectral SWIR data) of gradients (especially within a given geologic unit) in white mica absorption wavelength (Tschermak substitution) represents a potential vectoring tool for the discovery of new Archean hydrothermal gold systems.