Hyperspectral System Research Articles

Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study.

Hyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery. A lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350-1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study. Focusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery. This work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.

Compressive hyperspectral microscopy for cancer detection.

Hyperspectral microscopy grants the ability to characterize unique properties of tissues based on their spectral fingerprint. The ability to label and measure multiple molecular probes simultaneously provides pathologists and oncologists with a powerful tool to enhance accurate diagnostic and prognostic decisions. As the pathological workload grows, having an objective tool that provides companion diagnostics is of immense importance. Therefore, fast whole-slide spectral imaging systems are of immense importance for automated cancer prognostics that meet current and future needs. We aim to develop a fast and accurate hyperspectral microscopy system that can be easily integrated with existing microscopes and provide flexibility for optimizing measurement time versus spectral resolution. The method employs compressive sensing (CS) and a spectrally encoded illumination device integrated into the illumination path of a standard microscope. The spectral encoding is obtained using a compact liquid crystal cell that is operated in a fast mode. It provides time-efficient measurements of the spectral information, is modular and versatile, and can also be used for other applications that require rapid acquisition of hyperspectral images. We demonstrated the acquisition of breast cancer biopsies hyperspectral data of the whole camera area within . This means that a typical biopsy can be measured in . The hyperspectral images with 250 spectral bands are reconstructed from 47 spectrally encoded images in the spectral range of 450 to 700nm. CS hyperspectral microscopy was successfully demonstrated on a common lab microscope for measuring biopsies stained with the most common stains, such as hematoxylin and eosin. The high spectral resolution demonstrated here in a rather short time indicates the ability to use it further for coping with the highly demanding needs of pathological diagnostics, both for cancer diagnostics and prognostics.

Hyperspectral imaging combined with CNN for maize variety identification.

As the third largest food crop in the world, maize has wide varieties with similar appearances, which makes identification difficult. To solve the problem of identification of hybrid maize varieties, a method based on hyperspectral image technology combined with a convolutional neural network (CNN) is proposed to identify maize varieties. In this study, 735 maize seeds from seven half-parent hybrid maize varieties were regarded as the research object. The maize seed images in the range of 900 ~ 1700nm were obtained by hyperspectral image acquisition system. The region of interest (ROI) of the embryo surface was selected, and the spectral reflectance of maize seeds was extracted. After Savitzky-Golay (SG) Smoothing pretreatment, Maximum Normalization (MN) pretreatment was performed. The 56 feature wavelengths were selected by Competitive Adaptive Reweighting Algorithm (CARS) and Successive Projection Algorithm (SPA). And the 56 wavelengths were mapped to high-dimensional space by high-dimensional feature mapping and then reconstructed into three-dimensional image features. A five-layer convolution neural network was used to identify three-dimensional image features, and nine (SG+MN)-(CARS+SPA)-CNN maize variety identification models were established by changing the input feature dimension and the depth factor size of the model layer. The results show that the maize variety classification model works best, when the input feature dimension is 768 and the layer depth factor d is 1.0. At this point, the model accuracy of the test set is 96.65% and the detection frame rate is1000 Fps/s in GPU environment, which can realize the rapid and effective non-destructive detection of maize varieties. This study provides a new idea for the rapid and accurate identification of maize seeds and seeds of other crops.

Identification of tea quality at different picking periods: A hyperspectral system coupled with a multibranch kernel attention network

The material content and nutritional composition of tea vary during different picking periods, leading to variations in tea quality. The absence of rapid evaluation methods for identifying tea quality at different picking periods hinders the smooth operation and maintenance of agricultural production and market sales. In this work, hyperspectral technology combined with the multibranch kernel attention network (MBKA-Net) is proposed to identify the overall quality of tea during different picking periods. First, spectral information of six different tea picking periods is obtained using a hyperspectral system. Second, the multibranch kernel attention (MBKA) method is proposed, which effectively mines spectral features through multiscale adaptive extraction and achieves classification of tea at different picking periods. Finally, MBKA-Net achieves outstanding performance with 96.18% accuracy, 97.14% precision, and 97.18% recall. In conclusion, MBKA-Net combined with a hyperspectral system provides an effective detection method for identifying the quality of tea at different picking periods.

The Detection of Kiwifruit Sunscald Using Spectral Reflectance Data Combined with Machine Learning and CNNs

Sunscald in kiwifruit, an environmental stress caused by solar radiation during the summer, reduces fruit quality and yields and causes economic losses. The efficient and timely detection of sunscald and similar diseases is a challenging task but helps to implement measures to control stress. This study provides high-precision detection models and relevant spectral information on kiwifruit physiology for similar statuses, including early-stage sunscald, late-stage sunscald, anthracnose, and healthy. Primarily, in the laboratory, 429 groups of spectral reflectance data for leaves of four statuses were collected and analyzed using a hyperspectral reflection acquisition system. Then, multiple modeling approaches, including combined preprocessing methods, feature extraction algorithms, and classification algorithms, were designed to extract bands and evaluate the performance of the models to detect the statuses of kiwifruit. Finally, the detection of different stages of kiwifruit sunscald under anthracnose interference was accomplished. As influential bands, 694–713 nm, 758–777 nm, 780–799 nm, and 1303–1322 nm were extracted. The overall accuracy, precision, recall, and F1-score values of the models reached 100%, demonstrating an ability to detect all statuses with 100% accuracy. It was concluded that the combined processing of moving average and standard normal variable transformations (MS) could significantly improve the data; the near-infrared support vector machine and visible convolutional neural network with MS (NIR-MS-SVM and VIS-MS-CNN) were established as high-precision detection techniques for the classification of similar kiwifruit statuses, demonstrating 25.58% higher accuracy than the single support vector machine. The VIS-MS-CNN model reached convergence with a stable cross-entropy loss of 0.75 in training and 0.77 in validation. The techniques developed in this study will improve orchard management efficiency and yields and increase researchers’ understanding of kiwifruit physiology.

FGRC-Net: A high-information interactive convolutional neural network for identifying ink spectral information

Modifying some keywords or numbers on documents to change the original intention is illegal. In some litigation cases, especially economic cases, there is often a need to examine the type of ink on documents. This paper proposes a nondestructive and accurate detection method for ink inspection. In this work, a high-information interactive, full group residual convolution network (FGRC-Net) is proposed and combined with spectral information to classify the inks of six different brands of pens. First, FGRC-Net realizes the information interaction among channels to effectively improve the feature extraction ability through multigroup convolution and multipath cascade. Meanwhile, to avoid feature degradation, the residual connection is introduced. Second, the ink spectral information of six pens based on the hyperspectral system is obtained. Finally, in comparing FGRC-Net and other deep learning methods, FGRC-Net shows the best classification performance with 98.38% accuracy, 98.53% precision, and 98.40% recall. The results show that combining FGRC-Net with a hyperspectral system is an effective detection method for handwritten ink classification. It also provides an effective detection method for handwritten document ink inspection. At the same time, the universality and validity of FGRC-Net in processing spectral information are verified by the comparison of multiple datasets.

Vision-Aided Hyperspectral Full-Waveform LiDAR System to Improve Detection Efficiency

The hyperspectral full-waveform LiDAR (HSL) system based on the supercontinuum laser can obtain spatial and spectral information of the target synchronously and outperform traditional LiDAR or imaging spectrometers in target classification and other applications. However, low detection efficiency caused by the detection of useless background points (ULBG) hinders its practical applications, especially when the target is small compared with the large field of view (FOV) of the HSL system. A novel vision-aided hyperspectral full-waveform LiDAR system (V-HSL) was proposed to solve the problem and improve detection efficiency. First, we established the framework and developed preliminary algorithms for the V-HSL system. Next, we experimentally compared the performance of the V-HSL system with the HSL system. The results revealed that the proposed V-HSL system could reduce the detection of ULBG points and improve detection efficiency with enhanced detection performance. The V-HSL system is a promising development direction, and the study results will help researchers and engineers develop and optimize their design of the HSL system and ensure high detection efficiency of spatial and spectral information of the target.

A hyperspectral plant health monitoring system for space crop production.

Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.

A tunnel coaxial 3D hyperspectral scanning system for underground mine investigation

A hyperspectral scanning system was developed for three-dimensional (3D) surface mapping in underground spaces, such as mine shafts and tunnels. A hyperspectral line-scanning camera was mounted on the rotating driver unit coaxial with the tunnel to image both the mine wall and the ceiling. Uniform light was illuminated on the target surface to be imaged using a halogen lamp rotating together with the hyperspectral imaging sensor. Inertial Measuring Unit (IMU) was also attached to the sensor unit together with the hyperspectral camera so that sensor’s geometric information could be acquired simultaneously during imaging. All sensor and controller units were mounted on a cart-type platform for easy movement in the tunnel, and a battery mounted on the platform supplied power for system operation and the halogen light source. The developed scanning system was tested in an actual mine, and 3D hyperspectral images of the internal surface of the mine shaft were successfully obtained.

Development of an Optical System with an Orientation Module to Detect Surface Damage to Potato Tubers

This method is a variant of non-destructive multiparametric surface analysis and includes the implementation of hyperspectral and RGB image processing approaches from different angles. This work is based on a fundamental hyperspectral survey system for obtaining data on scanned biological objects in many spectral ranges and with several possible variants of assembling a system with different types of surface illumination with point light and diffuse illumination. The implementation of the technology through the use of a diffused light source provides the diffuse illumination of a biological object with pronounced symptoms of rarefaction on the surface of a biological object—in this case, potato tubers, due to the presence of signs of disease on the potato peel, as well as their deformation. Using broadband lamps, a short-pass filter is located between the source and the object (λ ≤ 400 nm, λ may vary depending on the excitation length), and a long-pass filter (λ ≥ 400 nm) between the root or onion and the chamber. The use of a vision system with a created database containing models of real defects in potato tubers showed a high sorting efficiency, providing an accuracy of sorting by size of 95.4%, and an accuracy by the presence of defects of 93.1%.

Characterization of the layered SIF distribution through hyperspectral observation and SCOPE modeling for a subtropical evergreen forest

Solar-induced chlorophyll fluorescence (SIF) has been widely used as a prospective proxy of plant photosynthesis to accurately detect the response of vegetation to climate change and study terrestrial ecosystem carbon cycle. However, the challenge of estimating vegetation gross primary product (GPP) based on SIF observations remains unresolved due to the confounding effects of leaf physiology and canopy structure, especially the vertical heterogeneity of plant attributes. Radiative transfer models are ideally suited to investigate the variability in radiance signals including fluorescence, but previous studies lack vertically layered spectra to validate the model. In this study, we developed a vertically layered hyperspectral system and proposed an innovative framework for assessing vertical characteristics of SIF and improving the estimation of GPP based on SIF in a subtropical evergreen forest. The global sensitivity analysis on Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model with vertical profiles showed that chlorophyll content (Cab), leaf inclination distribution (LIDFa), leaf area index (LAI), and senescent material (Cs) dominate the vertical variations in reflectance (Ref) and SIF. We found that the vertical characteristics of SIF were mainly impacted by these parameters of the adjacent vertical layer, except for the effects of LAI across the vertical layers on the observed understory SIFU, which were approximately equivalent. 7.8 ± 1.7% of the SIFU was transferred to the top of canopy, contributing up to 17% of the SIF observed on the top of canopy (SIFTOC) during the year. Furthermore, our findings suggest that substituting the observed SIFTOC with the total emitted SIF, particularly from the simulated overstory and understory SIF, could enhance the correlation between GPP and SIF with an increased R2 (ΔR2 = 0.09). This study highlights the importance of accounting for the contribution of the layered SIF in the quantification of the total SIF emission and can benefit the GPP estimation based on SIF signals in subtropical evergreen forests with high canopy density.

An independent validation reveals the potential to predict Hagberg–Perten falling number using spectrometers

AbstractThe Hagberg–Perten falling number (HFN) method is the international standard used to evaluate the damage to wheat (Triticum aestivum) grain quality due to preharvest sprouting (PHS) and late maturity alpha‐amylase (LMA). However, the HFN test requires specialized laboratory facilities and is time consuming. Spectrometers were known as a potential tool for quick HFN assessment, but none of the studies have validated the assessment results across different datasets. In this study, an independent validation was conducted using independent samples and spectral instruments. The calibration set had 462 grain samples of 92 varieties grown at 24 locations in 2019 and examined using a near‐infrared spectrometer. In the validation set, 19 varieties collected from 10 locations in 2 years that experienced either PHS or LMA were scanned with a hyperspectral camera. The association between spectra and HFN was modeled by partial least square regression. As a result, the independent validation correlation accuracy was r = 0.72 and a mean absolute error of 56 s. Furthermore, this study showed a cost‐effective alternative using only 10 spectral bands to predict HFN, and it achieved better performance than the full spectrum of the hyperspectral system. In conclusion, this is the first study that showed the potential that wheat HFN could be predicted on an independent dataset measured by a different instrument. The result suggested that spectrometers can potentially serve as a faster alternative for plant breeders to develop varieties resistant to PHS and LMA, and for growers to screen damaged grains in transportation processes.

A rapid aroma quantification method: Colorimetric sensor-coupled multidimensional spectroscopy applied to black tea aroma

Aroma affects the quality of black tea, and the rapid evaluation of aroma quality is the key to realize the intelligent processing of black tea. A simple colorimetric sensor array coupled with a hyperspectral system was proposed for the rapid quantitative detection of key volatile organic compounds (VOCs) in black tea. Feature variables were screened based on competitive adaptive reweighted sampling (CARS). Furthermore, the performance of the models for VOCs quantitative prediction was compared. For the quantitative prediction of linalool, benzeneacetaldehyde, hexanal, methyl salicylate, and geraniol, the CARS–least-squares support vector machine model's correlation coefficients were 0.89, 0.95, 0.88, 0.80, and 0.78, respectively. The interaction mechanism of array dyes with VOCs was based on density flooding theory. The optimized highest occupied molecular orbital levels, lowest unoccupied molecular orbital energy levels, dipole moments, and intermolecular distances were determined to be strongly correlated with interactions between array dyes and VOCs.

Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique

Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.

Rapid detection of white blood cells using hyperspectral microscopic imaging system combined with Multi-data Faster RCNN

Blood test is a basic medical test item, and the content of white blood cells (WBCs) is directly related to the evaluation of the patient's health and the judgment of serious diseases. However, both manual detection method and traditional image recognition method have the problem of insufficient efficiency and accuracy. The purpose of this study is to propose a fast and accurate WBC detection method, called hyperspectral based Multi-data Faster RCNN. For hyperspectral multi-dimensional sensing information, one-dimensional (1-D) CNN and Faster RCNN were used to model and analyze spectral and image features, respectively. And then, the Multi-data Faster RCNN was built, in which the 1-D CNN network and fusion network layer were added in Faster RCNN to realize the joint detection based on spectral and image fusion features of WBCs. The hyperspectral system coupled with Multi-data Faster RCNN can effectively improve the accuracy of WBCs’ recognition. The average precision (Ap) value and the F1 score of each classification of WBCs were above 0.9, and the mean average precision (mAP) even reached 0.962. The results showed that the Multi-data Faster RCNN integrated with hyperspectral imaging had great potential in the detection of WBCs.

Terahertz reflection hyperspectral 3D imaging using beam scanning.

Terahertz (THz) radiation has the capability to combine spectroscopy and imaging in a single system. The resulting hyperspectral images can reveal concealed objects and identify materials by means of characteristic spectral features. For security applications, THz is attractive for its non-contact and non-destructive measurement capabilities. For such applications, objects may be too absorbing for transmission measurements, or only one side of an object may be accessible, necessitating a reflection measurement configuration. This work details the development and demonstration of a compact fiber-coupled hyperspectral imaging reflection system suited to field use for security and industrial applications. The system uses beam steering to measure objects of up to 150 mm diameter with a depth range of up to 255 mm, allowing for 3-dimensional mapping of objects, while simultaneously acquiring spectral data. Spectral information between 0.2-1.8 THz is extracted from a hyperspectral image and used to identify lactose, tartaric acid, and 4-aminobenzoic acid in high and low humidity environments.

An attention-driven nonlinear optimization method for CS-based hyperspectral image reconstruction

Hyperspectral images contain both spatial and spectral information, which makes it has great advantages in the field of target identification, material determination, etc. However, hyperspectral image acquisition systems usually output massive amounts of data. Compressed sensing (CS) and single dispatchers coded aperture snapshot spectral imaging (SD-CASSI) were proposed to compress the amount of data. These systems usually use numerical optimization methods to do image reconstruction by finding the optimal reconstructed image. This method generates a large number of iterative calculations, which is time-consuming. In this paper, we modeled the image reconstruction for SC-CASSI as a non-linear optimization problem and proved that the number of iterations of the solution process is exponential. We proposed an attention-driven reconstruction algorithm for SD-CASSI systems. The algorithm mainly consists of an initial convolution block and residual attention modules. A channel attention module is also introduced to mine spectrally correlated features in raw signals. Experimental results show that the proposed algorithm is superior to three states-of-art reconstruction algorithms for CS-based systems. The reconstruction speed of the system is also increased by 94% compared with the numerical method.

Spectrally-shaped illumination for improved optical inspection of lateral III-V-semiconductor oxidation.

We report an hyperspectral imaging microscopy system based on a spectrally-shaped illumination and its use to offer an enhanced in-situ inspection of a technological process that is critical in Vertical-Cavity Surface-Emitting Laser (VCSEL) manufacturing, the lateral III-V-semiconductor oxidation (AlOx). The implemented illumination source exploits a digital micromirror device (DMD) to arbitrarily tailor its emission spectrum. When combined to an imager, this source is shown to provide an additional ability to detect minute surface reflectance contrasts on any VCSEL or AlOx-based photonic structure and, in turn, to offer improved in-situ inspection of the oxide aperture shapes and dimensions down to the best-achievable optical resolution. The demonstrated technique is very versatile and could be readily extended to the real-time monitoring of oxidation or other semiconductor technological processes as soon as they rely on a real-time yet accurate measurement of spatio-spectral (reflectance) maps.

RDB-CSCA: A deep learning method to identify the spectral information for tracing the egg quality

It is important to provide a fast and nondestructive detection method for egg quality supervision to realize the quality identification of eggs in different storage periods based on sensing detection technology. In this paper, a detection method is proposed for egg quality identification in different storage periods based on a deep learning method combined with a hyperspectral system. First, channel-spatial collaborative attention (CSCA) is proposed to adaptively focus on the critical features of deep spectral information. Second, residual dense block (RDB) network is introduced to adaptively fuse deep and shallow features, thereby avoiding the feature degradation phenomenon that occurs in the convolution calculation process. Finally, the CSCA and RDB are combined to construct a decision-making network for the identification of spectral information of eggs in different storage periods. The results show that in the comparison of multi-structure models, RDB-CSCA achieves the best classification performance, with the classification accuracy of 96.88%, precision of 97.18%, recall of 97.61%, and F1 score of 97.39%.

Hyperspectral core scanner: An effective mineral mapping tool for apatite in the Upper Zone, northern limb, Bushveld Complex

The technological advances in efficient, rapid, and non-destructive hyperspectral core logging systems for systematic mineral mapping have led to the discovery and exploitation of new mineral deposits Hyperspectral imaging in the long-wave infrared range has been recently used successfully to identify various phosphate-bearing minerals (monazite, xenotime, and britholite), with limited work on apatite associated with mafic-ultramafic layered intrusions. In this study we investigate the effectiveness of a hyperspectral imaging (HSI) system with long-wave infrared (LWIR) bandwidthsto identify apatite in the Upper Zone of the Bushveld Complex. The accuracy of the HSI results was validated by mineralogical and geochemical data. The two apatite-enriched zones detected by HSI suggesting widespread development of apatite throughout the uppermost 600 m of the Upper Zone. The lower apatite-enriched zone is approximately 40 m thick, while the upper apatite-enriched zone is about 23 m thick, consistent with previous thickness determinations by traditional logging and analytical methods. Spectral mixing observed in the response of apatite is ascribed either to the common association of apatite and olivine in these rocks, or to differences between the spatial resolution of the hyperspectral image and the size of apatite grains. The VNIR-SWIR wavelength region did not show prominent spectral features of apatite. Nonetheless, HSI in the LWIR range is effective in mapping apatite and should therefore be considered as an exploration tool. This research advances our knowledge of the reflectance spectroscopy of REE-bearing minerals, which makes it easier to detect, identify, and quantify REE-bearing silicate minerals by HSI.