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Phase-change Tunable Filters (PCTFs) for Hyperspectral Thermal Imaging

In this presentation, we introduce phase-change tunable filters (PCTFs), which are capable of actively tunable spectral filtering as well as continuous tuning (hyperspectral) across multiple IR wavebands (1–10 μm); several orders-of-magnitude faster switching speeds (GHz–MHz) than the state-of-the-art, and narrowband filtering (~50 nm passband). This technology subsystem is based on the unique electro-optics of non-volatile, phase-change materials (PCMs) such as GeSbTe and SbS. PCMs exhibit a large, reversible refractive index shift through applied energetic pulses. By integrating PCMs with established optical filter designs—whose optical properties are strongly dependent on the constituent materials’ refractive indices—we can rapidly and reversibly tune the PCTFs center wavelength (passband).

Tunable filter↗

NASA NTRS1995

AOTF Polarimetric Hyperspectral Imaging for Mine Detection.

This paper reports an analysis of polarimetric hyperspectral imagery of an outdoor scene containing a number of inactive mines. The imagery was taken with a visible/near-infrared acousto-optic tunable filter prototype system. The result has illustrated that the characteristics of mine smooth surfaces can create high signals in polarization difference images and consequently provide a quick and reliable approach for detection.

['Mine↗

NASA NTRS1994

Analysis of AOTF Hyperspectral Imagery

This paper reports results from an analysis of polarimetric hyperspectral imagery collected using a prototype acousto-optic tunable filter (AOTF) instrument in an outdoor environment. Spectra, derivative spectra, and polarization spectra in the image cube form were studied. The issue concerns spectral and polarization signatures of vegetation, contributions due to aerosol, and man-made object detection. The result illustrates potentials of the technology for a variety of remote sensing applications.

acousto-optic↗

NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: An Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, as well as improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument – the Ocean Color Instrument (OCI) – is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This includes continuous collection of spectra from 340 nm to 890 nm in 5 nm steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a discussion of the capabilities and design concept of OCI.

hyperspectral radiometer↗

NASA NTRS2023

A Blind Convolutional Deep Autoencoder for Spectral Unmixing of Hyperspectral Images Over Waterbodies

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and public health. Automatic identification based on remote sensing hyperspectral image analysis provides a valuable mechanism for extracting the spectral signatures of harmful algal blooms and their respective percentage in a region of interest. This paper proposes a new model called a non-symmetrical autoencoder for spectral unmixing to perform endmember extraction and fractional abundance estimation. The model is assessed in benchmark datasets, such as Jasper Ridge and Samson. Additionally, a case study of the HSI2 image acquired by NASA over Lake Erie in 2017 is conducted for extracting optical water types. The results using the proposed model for the benchmark datasets improve unmixing performance, as indicated by the spectral angle distance compared to five baseline algorithms. Improved results were obtained for various metrics. In the Samson dataset, the proposed model outperformed other methods for water (0.060) and soil (0.025) endmember extraction. Moreover, the proposed method exhibited superior performance in terms of mean spectral angle distance compared to the other five baseline algorithms. The non-symmetrical autoencoder for the spectral unmixing approach achieved better results for abundance map estimation, with a root mean square error of 0.091 for water and 0.187 for soil, compared to the ground truth. For the Jasper Ridge dataset, the non-symmetrical autoencoder for the spectral unmixing model excelled in the tree (0.039) and road (0.068) endmember extraction and also demonstrated improved results for water abundance maps (0.1121). The proposed model can identify the presence of chlorophyll-a in waterbodies. Chlorophyll-a is an essential indicator of the presence of the different concentrations of macrophytes and cyanobacteria. The non-symmetrical autoencoder for spectral unmixing achieves a value of 0.307 for the spectral angle distance metric compared to a reference ground truth spectral signature of chlorophyll-a. The source code for the proposed model, as implemented in this manuscript, can be found at https://github.com/EstefaniaAlfaro/autoencoder_owt_spectral.git.

hyperspectral imaging↗

NASA NTRS2024

The Emerging Issue-3 Revision of the Ccsds-123.0-B Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard

The CCSDS-123.0-B Low-Complexity Lossless and Near-Lossless Multispectral & Hyperspectral Image Compression standard provides state-of-the-art compression for imaging spectrometer data. Issue 1 of this standard, published in 2012, provides only lossless compression capability. In 2019, Issue 2 was published, adding new features and in particular augmenting the compressor to provide also near-lossless compression, i.e., compression with a user-specified error limit in each spectral band. This presentation will describe the Issue 3 revision currently under development by the CCSDS Data Compression working group. This revision will provide region-of-interest compression capability. That is, a user-provided spatial classification map can be used to specify different data fidelity parameters in different image regions. This would, for example, allow an onboard classification algorithm to identify the most important regions of an image, or regions obscured by clouds, and adjust compression fidelity accordingly in each region to maximize value of imaging spectrometer data returned over constrained space communication links. The revision also aims to add an option that is unrelated to region-of-interest compression but can improve compression effectiveness in some cases.

CCSDS Hyperspectral↗

DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗

DOE OSTI2025

Hyperspectral Cathodoluminescence of Bastnäsite at the Mountain Pass Mine, USA: Implications for REE Mineralisation and Mining

This study uses micro-analyses including hyperspectral cathodoluminescence to image and obtain mineral spectral to help evaluate REE deportment via from Mountain Pass Mine in California. It presents the first known CL-based spectra for bastnäsite, new information regarding baryte CL spectra, providing insights on the coeval and spatial relationship between REE, Ba, and S in carbonatite systems. Bastnäsite, baryte, and monazite are often intergrown in both brecciated, “blue ore” samples, and these minerals occur as an apparent intercumulus texture in the dolomite-rich, “white ore” samples. Bastnäsite luminescence is present, but very low compared to baryte in the visible spectrum. Bastnäsite illustrates some textural variability related to its main luminescent peak ~1.4 eV, which is indicative of Nd3+. It also exhibits several other small peaks comprised of multiple activators related to various REE, including Ce3+, Sm3+, Eu3+, and potentially Eu2+. Baryte displays textures of variable complexity but often with darker rims near crystal margins. It luminesces mainly at 1.4-1.52 eV, 2.05 eV, and 3.59-4.08 eV. The 2.05 eV peak is likely attributed to Ag+, but the other peaks are more enigmatic.

58 - GEOSCIENCES↗

DOE OSTI2022

Visualization of a Limonene Synthesis Metabolon Inside Living Bacteria by Hyperspectral SRS Microscopy

Abstract Monitoring biosynthesis activity at single‐cell level is key to metabolic engineering but is still difficult to achieve in a label‐free manner. Using hyperspectral stimulated Raman scattering imaging in the 670–900 cm −1 region, localized limonene synthesis are visualized inside engineered Escherichia coli . The colocalization of limonene and GFP‐fused limonene synthase is confirmed by co‐registered stimulated Raman scattering and two‐photon fluorescence images. The finding suggests a limonene synthesis metabolon with a polar distribution inside the cells. This finding expands the knowledge of de novo limonene biosynthesis in engineered bacteria and highlights the potential of SRS chemical imaging in metabolic engineering research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

ClassNMSW- a real-time classification approach for non-recycled municipal solid waste using hyperspectral imaging

Real-time classification of non-recycled municipal solid waste (NMSW) is essential for efficient valorization. This study introduces ClassNMSW, a comprehensive framework for classifying 22 NMSW subclasses under industrial constraints by using hyperspectral imaging (HSI). A primary innovation of this work is the development of a variance-controlled spectral extraction algorithm. Unlike traditional methods that rely on simple averaging, this approach systematically investigates the extent of pixel extraction to minimize the loss of critical chemical information while maximizing data reduction thus ensuring high spectral fidelity with low computational cost. The approach developed in this work integrates automated, computer-vision-based background removal, eliminating the need for the manual thresholding common in current literature. To resolve ambiguities among chemically similar subclasses, a tiered classification and multi-camera fusion strategy (NIR17 and NIR22) is implemented. Results demonstrate that ClassNMSW achieves an object-wise weighted accuracy of 98.70% for single-sensor configurations and 100% under sensor fusion. A novel rolling-window strategy satisfies desired end-to-end latency of <2 s, satisfying the strict deterministic requirements of high-speed industrial sorting environments. The ClassNMSW framework provides a scalable foundation for advancing circularity and resource recovery in large-scale waste valorization operations.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Revealing the Chemical Bonding in Adatom Arrays via Machine Learning of Hyperspectral Scanning Tunneling Spectroscopy Data

The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co 3 Sn 2 S 2 cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co 3 Sn 2 S 2 surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Features of correlation measurements of the parameters of pulsed hyperspectral optical fields using an asymmetric interferometer

Differences in correlation measurements of the parameters of pulsed hyperspectral optical fields using symmetric and asymmetric interferometers are considered. It is shown analytically that the resulting cross-correlation function is sensitive to phase perturbations in the original wave field. The considered setup, which contains a telescopic reflective 4f system of parabolic mirrors in one arm, demonstrates that in the case of an asymmetric interferometer, the presence of aberrations leads to degradation of the reconstructed image, whereas in the case of symmetric interferometers these aberrations do not affect the result. (paper)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Detection of the Metabolic Response to Drought Stress Using Hyperspectral Reflectance

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49 to 0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between droughted and watered plants based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Temperature Measurement of Laser-Irradiated Metals Using Hyperspectral Imaging

Accurate noncontact surface-temperature measurements during laser-based materials processing remain challenging due to the difficulty of establishing reliable emissivity values as a function of temperature and wavelength. Direct measurement of emissivity is difficult, as the emissivity may be changing constantly in the laser-material interaction region, where the temperature gradients are extreme and surface displacement can complicate the measurement. Here, we present a hyperspectral imaging method using a multiwavelength camera to capture the spectral radiance in eight different bands and fit the wavelength-dependent radiance to Planck’s law. Time-resolved temperature measurements during microsecond pulsed-laser irradiation of a metal plate made of the titanium alloy Ti -6 Al -4 V provide temperature information about the irradiated surface with an estimated accuracy of ± 10 % . The extracted wavelength-dependent emissivity slope compares well with reported results for macroscale titanium melts obtained using IR spectroscopy, which measure temperature without any built-in assumptions about the emissivity. The results are directly applicable to temperature monitoring during welding, additive manufacturing, and semiconductor growth.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Hyperspectral X-ray Imaging: Progress Towards Chemical Analysis in the SEM

The Hyperspectral X-ray Imaging (HXI) project will enable non-destructive chemical-state determination of nano-scale samples in the electron microscope for nuclear safeguards applications. To efficiently measure chemical state through electron-excited X-ray emission requires a combination of wide spectral bandwidth, high resolution, and high count rate capability. We are building a next-generation X-ray detector based on an array of transition edge sensors (TESs) to make these measurements possible and routine in the scanning electron microscope (SEM). Leveraging the large pixel densities afforded by microwave multiplexing readout and continuous, uninterrupted operation of a cryogen-free dilution refrigerator, this instrument will have efficiency to allow chemical species identification of nano-scale samples in hours instead of days to weeks. We describe prototype pixel designs for this HXI instrument, comprising three pixel types that will make up the hybrid TES array. Engineering design of the integrated HXI cryostat and SEM system is in progress, with full detector commissioning expected in Spring 2021 followed closely by full-scale integration with the SEM. Furthermore, we also report on the commissioning of a complementary TES-based X-ray emission spectroscopy platform for bulk samples to build a spectral library for HXI sample identification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Preliminary hyperspectral neutron radiography of binder jet 3D printed and melt infiltrated Si–SiC

The preliminary results of hyperspectral neutron radiography of Si–SiC made with binder jet additive manufacturing, subsequent carbon addition, and silicon melt infiltration are presented. The samples were measured in the processing states before and after the melt infiltration to determine if neutrons would help characterize the microstructure to better understand the melt infiltration process. Theoretical Bragg edges were calculated for comparison with the radiographic results. The changes in the Bragg edges from pre- to post-infiltrated reaction-bonded SiC were evident, that could be used to address the differences in microstructures. In conclusion, the preliminary results presented in this study demonstrated that neutron Bragg-edge imaging is possible with Si–SiC reaction-bonded ceramics and can help determine the microstructure of melt infiltrated samples.

SiC↗

DOE OSTI2023

A Nowcasting Approach for Low-Earth-Orbiting Hyperspectral Infrared Soundings within the Convective Environment

Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. Here, we have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership (Suomi NPP) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Rapid simulations of hyperspectral near-field images of three-dimensional heterogeneous surfaces

The scattering-type scanning near-field optical microscope (s-SNOM) has emerged as a powerful tool for resolving nanoscale inhomogeneities in laterally heterogeneous samples. However, most analytical models used to predict the scattering near-field signals are assuming homogenous landscapes (bulk materials), resulting in inconsistencies when applied to samples with more complex configurations. In this work, we combine the point-dipole model (PDM) to the finite-element method (FEM) to account for the lateral and vertical heterogeneities while keeping the computation time manageable. Full images, spectra, or hyperspectral line profiles can be simulated by calculating the self-consistent dipole radiation demodulated at higher harmonics of the tip oscillation, mimicking real experimental procedures. Using this formalism, we clarify several important yet puzzling experimental observations in near-field images on samples with rich typography and complex material compositions, heterostructures of two-dimensional material flakes, and plasmonic antennas. The developed method serves as a basis for future investigations of nano-systems with nontrivial topography.

36 MATERIALS SCIENCE↗