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At least 361 records · Page 20

Tunable Mid-wave Infrared Fabry-Perot Bandpass Filters Using Phase-Change GeSbTe

Tunable mid-wave infrared (MWIR) filters are highly desirable in a wide variety of imaging and sensing, as the MWIR contains numerous spectral signatures. We fabricated spectrally-tunable Fabry-Perot bandpass filters operating across the MWIR by utilizing the phase-change material GeSbTe (GST) as a tunable cavity medium between distributed Bragg reflectors. The induced refractive index modulation of the GST increases the cavity’s optical path length, tuning the passband. Our filters have 300-nm spectral tunability, 60-75% transmission efficiencies, and 50-65 nm narrowband FWHMs (Q-factor 70-90). We demonstrate multispectral thermal imaging and dynamic gas sensing by matching the filter’s passband (~ 4.25μm) to a CO2 vibrational-absorption mode.

Tunable filter↗

Mechanically flexible mid-wave infrared imagers using black phosphorus ink films

The mid-wave infrared (MWIR) spectral range (λ = 3–8 μm) enables important sensing and imaging applications, including non-invasive bioimaging, night vision, and autonomous navigation. Commercial MWIR photodetectors are limited to rigid imagers based on heteroepitaxial materials. There is an emerging need for mechanically flexible MWIR imagers to broaden their functionality and practicality. Recently, photodetectors using van der Waals (vdW) black phosphorus (BP) flakes have demonstrated highly sensitive room-temperature photodetection. Additionally, vdW materials are solution-processable, facilitating scalable processing and flexible device fabrication. In this work, we present flexible MWIR imagers consisting of photodiodes fabricated on thin plastic substrates using BP ink films. We demonstrate mechanically robust responsivity up to 2.5-mm bending radii and after 5000 bending cycles. Leveraging this flexibility, we achieve full-azimuthal imaging, detecting directional light sources with precision. These results establish a scalable approach for large-area, conformable MWIR imaging and pave the way for integration with flexible electronics.

Wijaya, Theodorus Jonathan↗

Acousto-optic tunable filter multispectral imaging system

This paper discusses recent activities of Jet Propulsion Laboratory in the development of a new type of remote sensing multispectral imaging instruments using acousto-optic tunable filter (AOTF) as programmable bandpass filter. This remote sensor provides real-time operation; observational flexibility; measurements of spectral, spatial, and polarization information using a single instrument; and compact, solid state structure without moving parts. Two microcomputer-controlled AOTF imaging spectrometer breadboard systems were designed and built. One operates in the wavelength range of 0.48-0.76 micron and the other in the range of 1.2-2.5 micron. Experiments were performed using these two systems to observe geological and botanical objects in laboratory and outdoor environment. Results have demonstrated the feasibility of using the AOTF multispectral imaging system as a real-time versatile remote sensor with operational flexibility for future Army tactical applications.

Cheng, Li-Jen↗

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↗

The Next Frontier for Remote Sensing of Freshwater HABS: Utilizing Imaging Spectroscopy Data to Identify Cyanobacteria Bloom Types

Cyanobacteria Harmful Algal Blooms (HABs) and their associated toxicity are a concern for inland waters. Due to the extensive spatial coverage and frequent data availability of satellite-based sensors, multi-spectral remote sensing tools have demonstrated utility for monitoring, understanding, and managing these blooms. The next frontier is utilizing high spectral resolution imaging spectroscopy data, such as from NASA’s upcoming PACE, GLIMR, and SBG missions planned for this decade, which allow for the development of more sophisticated cyanobacteria detection algorithms. We evaluate the performance of cyanobacteria genera differentiation algorithms using precursor datasets in support of these upcoming hyperspectral missions. In situ measurements of lake optical, biological, chemical, and physical properties are used to characterize cyanobacteria blooms in hypereutrophic Clear Lake, CA, USA, which supports large, diverse algal and cyanobacteria populations. Data collection occurred during 12 field events in 2021-2022 across all seasons. Three field events were conducted concurrently with hyperspectral data acquisitions from the DESIS sensor on the International Space Station. The results of this study will support the development of future tools utilizing satellite-based imaging spectroscopy data to identify the cyanobacteria genera present in a bloom, and thus the potential for cyanotoxin production. This outcome – the ability to quickly characterize a cyanobacteria bloom at a low cost – will have enormous benefits for public health.

remote sensing↗

Computer vision-based rock bolt detection in orthomosaic imagery obtained in the Waste Isolation Pilot Plant underground facility

Assessing structural integrity of large underground tunnel facilities is often a time consuming and human-labor intensive task. Thus, research using various modes of sensing and automated detection of key structural components in mines is posed to aid in safety assessments and establishing overall structural health. We propose an approach utilizing off-the-shelf camera and lidar technology fixed to a custom sensing platform, image stitching techniques, fine-tuned object detection models, and specialized model-inference methods to automatically detect, count, and map roof bolts for assessment of structural safety in man-made underground tunnels. Results show a novel workflow for effective object counting in orthomosaic tunnel ceiling images generated from collections in GPS-denied mining environments. Additionally, we demonstrate effective fine-tuning of EfficientDet object detectors utilizing state-of-the-art image augmentation techniques known as the mosaic and mixup transformations. Our work is demonstrated on sensed data and imagery collected from the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) where miles of tunnel ceiling must be assessed for structural integrity.

42 ENGINEERING↗

New television camera eliminates vidicon tube

Small, lightweight camera systems use solid state imaging devices in the form of phototransistor mosaic sensors instead of vidicon tubes for light sensing and image conversion. The digital logic circuits scan the sensor mosaic at 60 frames per second to produce pictures composed of a series of dots rather than lines.

Source record↗

The Ring Current ENA Instrument on the NASA STORIE Mission

NASA has recently selected the Storm Time O+ Ring current Imaging Evolution (STORIE) mission to study the evolution of Earth’s ring current and the role of composition and dynamics during geomagnetic storms. STORIE addresses this inner magnetospheric science through the use of a Ring current Energetic Neutral Atom (RENA) instrument hosted on the International Space Station (ISS). This novel vantage point for ENA imaging has a number of scientific and implementation advantages. RENA is a high sensitivity remote sensing ENA imager and composition instrument with a field of view of 90 x 2 degrees, and an angular resolution of 2 x 2 degrees. The instrument aperture points zenith and its long dimension is oriented perpendicular to the ISS ram direction. As a result, RENA acts as a “push broom” imager building up a complete sky scan of the ring current in a single 90-minute ISS orbit. The instrument employs electrostatic optics along with a foil-MCP TOF x PHA x Delay-Line position measurement technique to resolve H and O energy spectra in the energy range 10-500 KeV for each look direction. Furthermore, it employs triple+ time and position coincidence to effectively reduce background noise from geocoronal and black sky UV as well as penetrating radiation. The abundant mass, power, and telemetry, resources of the ISS allow for large instrument aperture, very high sensitivity, complex data product and simplified operations, altogether contributing towards a high signal to noise (S/N) measurement.

Space Science↗

Compensation for the Atmosphere in Radiance Measured by the Airborne Visible/Infrared Imaging Spectrometer and Applications to an Advanced Land Remote Sensing System

The Airborne Visible/Infrared Imaging Spectrometer measures spatial images of the total upwelling spectral radiance from 400 to 2500 nm through 10 nm spectral channels. Quantitative research and application objectives for surface investigations require conversion of the measured radiance to surface reflectance or surface leaving radiance. To calculate apparent surface reflectance an estimation of atmospheric water vapor abundance, cirrus cloud effects, surface pressure elevation and aerosol optical depth is also required. Algorithms for the estimation of these parameters from the AVIRIS data themselves are described. Based upon these determined atmospheric parameters we show an example of the calculation of apparent surface reflectance from the AVIRIS-measured radiance using a radiative transfer code.

Airborne Visible/Infrared Imaging Spectrometer AVI↗

Far-field speckle correlations as a function of object position for microscopically distinguishing objects hidden in a randomly scattering medium

Super-resolution optical sensing is of critical importance in science and technology and has required prior information about an imaging system or obtrusive near-field probing. Additionally, coherent imaging and sensing in heavily scattering media such as biological tissue has been challenging, and practical approaches have either been restricted to measuring the field transmission of a single point source, or to where the medium is thin. We present the concept of far-subwavelength spatial sensing with relative object motion in speckle as a means to coherently sense through heavy scatter. Experimental results demonstrate the ability to distinguish nominally identical objects with nanometer-scale translation while hidden in randomly scattering media, without the need for precise or known location and with imprecise replacement. The theory and supportive illustrations presented provide the basis for super-resolution sensing and the possibility of virtually unlimited spatial resolution, including through thick, heavily scattering media with relative motion of an object in a structured field. This work provides enabling opportunities for material inspection, security, and biological sensing.

Hastings, Ryan L. (ORCID:0009000095977807)↗

An Integrated Multiangle, Multispectral, and Polarimetric Imaging Concept for Aerosol Remote Sensing from Space

Techniques for passive remote sensing of aerosol optical and microphysical properties from space include visible, near and shortwave-infrared imaging (e.g., from MODIS), multiangle intensity imaging (e.g., ATSR-2, AATSR, MISR), near-ultraviolet mapping (e.g., TOMSIOMI), and polarimetry (e.g., POLDER, APS). Each of these methods has unique strengths. In this paper, we present a concept for integrating these approaches into a unified sensor. Design goals include spectral coverage from the near-UV to the shortwave infrared; intensity and polarimetric imaging simultaneously at multiple view angles; global coverage within a few days; kilometer to sub-kilometer spatial resolution; and measurement of the degree of linear polarization (DOLP) for a subset of the spectral complement with an uncertainty of 0.5% or less.

polarimetric↗

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

NeMO=NET↗

Iterative-Transform Phase Retrieval Using Adaptive Diversity

A phase-diverse iterative-transform phase-retrieval algorithm enables high spatial-frequency, high-dynamic-range, image-based wavefront sensing. [The terms phase-diverse, phase retrieval, image-based, and wavefront sensing are defined in the first of the two immediately preceding articles, Broadband Phase Retrieval for Image-Based Wavefront Sensing (GSC-14899-1).] As described below, no prior phase-retrieval algorithm has offered both high dynamic range and the capability to recover high spatial-frequency components. Each of the previously developed image-based phase-retrieval techniques can be classified into one of two categories: iterative transform or parametric. Among the modifications of the original iterative-transform approach has been the introduction of a defocus diversity function (also defined in the cited companion article). Modifications of the original parametric approach have included minimizing alternative objective functions as well as implementing a variety of nonlinear optimization methods. The iterative-transform approach offers the advantage of ability to recover low, middle, and high spatial frequencies, but has disadvantage of having a limited dynamic range to one wavelength or less. In contrast, parametric phase retrieval offers the advantage of high dynamic range, but is poorly suited for recovering higher spatial frequency aberrations. The present phase-diverse iterative transform phase-retrieval algorithm offers both the high-spatial-frequency capability of the iterative-transform approach and the high dynamic range of parametric phase-recovery techniques. In implementation, this is a focus-diverse iterative-transform phaseretrieval algorithm that incorporates an adaptive diversity function, which makes it possible to avoid phase unwrapping while preserving high-spatial-frequency recovery. The algorithm includes an inner and an outer loop (see figure). An initial estimate of phase is used to start the algorithm on the inner loop, wherein multiple intensity images are processed, each using a different defocus value. The processing is done by an iterative-transform method, yielding individual phase estimates corresponding to each image of the defocus-diversity data set. These individual phase estimates are combined in a weighted average to form a new phase estimate, which serves as the initial phase estimate for either the next iteration of the iterative-transform method or, if the maximum number of iterations has been reached, for the next several steps, which constitute the outerloop portion of the algorithm. The details of the next several steps must be omitted here for the sake of brevity. The overall effect of these steps is to adaptively update the diversity defocus values according to recovery of global defocus in the phase estimate. Aberration recovery varies with differing amounts as the amount of diversity defocus is updated in each image; thus, feedback is incorporated into the recovery process. This process is iterated until the global defocus error is driven to zero during the recovery process. The amplitude of aberration may far exceed one wavelength after completion of the inner-loop portion of the algorithm, and the classical iterative transform method does not, by itself, enable recovery of multi-wavelength aberrations. Hence, in the absence of a means of off-loading the multi-wavelength portion of the aberration, the algorithm would produce a wrapped phase map. However, a special aberration-fitting procedure can be applied to the wrapped phase data to transfer at least some portion of the multi-wavelength aberration to the diversity function, wherein the data are treated as known phase values. In this way, a multiwavelength aberration can be recovered incrementally by successively applying the aberration-fitting procedure to intermediate wrapped phase maps. During recovery, as more of the aberration is transferred to the diversity function following successive iterations around the ter loop, the estimated phase ceases to wrap in places where the aberration values become incorporated as part of the diversity function. As a result, as the aberration content is transferred to the diversity function, the phase estimate resembles that of a reference flat.

Dean, Bruce H.↗

Image Registration for Stability Testing of MEMS

Image registration, or alignment of two or more images covering the same scenes or objects, is of great interest in many disciplines such as remote sensing, medical imaging. astronomy, and computer vision. In this paper, we introduce a new application of image registration algorithms. We demonstrate how through a wavelet based image registration algorithm, engineers can evaluate stability of Micro-Electro-Mechanical Systems (MEMS). In particular, we applied image registration algorithms to assess alignment stability of the MicroShutters Subsystem (MSS) of the Near Infrared Spectrograph (NIRSpec) instrument of the James Webb Space Telescope (JWST). This work introduces a new methodology for evaluating stability of MEMS devices to engineers as well as a new application of image registration algorithms to computer scientists.

Memarsadeghi, Nargess↗

Common-Path Wavefront Sensing for Advanced Coronagraphs

Imaging of faint companions around nearby stars is not limited by either intrinsic resolution of a coronagraph/telescope system, nor is it strictly photon limited. Typically, it is both the magnitude and temporal variation of small phase and amplitude errors imparted to the electric field by elements in the optical system which will limit ultimate performance. Adaptive optics systems, particularly those with multiple deformable mirrors, can remove these errors, but they need to be sensed in the final image plane. If the sensing system is before the final image plane, which is typical for most systems, then the non-common path optics between the wavefront sensor and science image plane will lead to un-sensed errors. However, a new generation of high-performance coronagraphs naturally lend themselves to wavefront sensing in the final image plane. These coronagraphs and the wavefront sensing will be discussed, as well as plans for demonstrating this with a high-contrast system on the ground. Such a system will be a key system-level proof for a future space-based coronagraph mission, which will also be discussed.

adaptive optics↗

An Image Retrieval and Processing Expert System for the World Wide Web

This paper presents a system that is being developed in the Laboratory of Applied Remote Sensing and Image Processing at the University of P.R. at Mayaguez. It describes the components that constitute its architecture. The main elements are: a Data Warehouse, an Image Processing Engine, and an Expert System. Together, they provide a complete solution to researchers from different fields that make use of images in their investigations. Also, since it is available to the World Wide Web, it provides remote access and processing of images.

Rodriguez, Ricardo↗

Multispectral Imaging Systems Using Acousto-Optic Tunable Filter

This paper discusses recent activities of Jet Propulsion Laboratory (JPL) in the development of a new type of remote sensing multispectral imaging instruments using acousto-optic tunable filter (AOTF) as programmable bandpass filter. This remote sensor provides real-time operation; observational flexibility; measurements of spectral, spatial, and polarization information using a single insturment; and compact, solid state structure without moving parts.

Multispectral↗