Dr. David W. Messinger Profile

Dr. David W. Messinger

Professor

SPIE Involvement:

Conference Chair | Track Chair | Author | Editor

Publications (141)

SPIE Journal Paper | 22 October 2024

Image quality and object detection performance

Austin Bergstrom, David Messinger

JEI, Vol. 33, Issue 05, 053051, (October 2024) https://doi.org/10.1117/12.10.1117/1.JEI.33.5.053051

KEYWORDS: Performance modeling, Image quality, Object detection, RGB color model, Education and training, Data modeling, Image classification, Image resolution, Computer vision technology, Sensors

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Proceedings Article | 8 June 2024 Presentation

Spectral imaging of medieval artifacts: collection, calibration, and analysis

David Messinger

Proceedings Volume 13031, 1303105 (2024) https://doi.org/10.1117/12.3032157

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Proceedings Article | 7 June 2024 Poster + Paper

Hyperspectral VNIR-SWIR image fusion on cultural heritage and remote sensing datasets using image sharpening techniques

Jose N. G. Macalintal, Amir Hassanzadeh, David Messinger

Proceedings Volume 13031, 130310O (2024) https://doi.org/10.1117/12.3012711

KEYWORDS: Short wave infrared radiation, Image fusion, Reflectivity, Image registration, Sensors, Cameras, Hyperspectral imaging, Image sensors, Remote sensing, Cultural heritage

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Proceedings Article | 7 June 2024 Presentation + Paper

Preserving legacies, pioneering frontiers: multi-sensor image fusion, from medieval manuscripts to UAS-based sensing

Amirhossein Hassanzadeh, Jose N. G. Macalintal, David Messinger

Proceedings Volume 13031, 1303109 (2024) https://doi.org/10.1117/12.3012402

KEYWORDS: Short wave infrared radiation, Data fusion, Sensors, Image registration, Spatial resolution, Infrared sensors, Elasticity, Image processing, Infrared radiation

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Proceedings Article | 14 June 2023 Presentation

A low-barrier-to-entry multispectral imaging system for the study of historical artifacts

David Messinger, Juilee Decker

Proceedings Volume PC12519, PC125190C (2023) https://doi.org/10.1117/12.2685053

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Proceedings Article | 13 June 2023 Presentation + Paper

Leveraging spatial content of images to enhance hyperspectral-multispectral fusion performance

Rey Ducay, David Messinger

Proceedings Volume 12519, 125190D (2023) https://doi.org/10.1117/12.2665172

KEYWORDS: Target detection, Image fusion, Detection and tracking algorithms, Hyperspectral imaging, Diffusion, Feature fusion, Clutter, Shadows, Edge detection

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Proceedings Article | 13 June 2023 Presentation + Paper

Image quality and object detection performance of convolutional neural networks

Austin Bergstrom, David Messinger

Proceedings Volume 12527, 125270M (2023) https://doi.org/10.1117/12.2663779

KEYWORDS: Image quality, Performance modeling, Object detection, Data modeling, Image classification, Image resolution, Computer vision technology, Signal to noise ratio

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SPIE Journal Paper | 10 April 2023

Image fusion of hyperspectral and multispectral imagery using nearest-neighbor diffusion

Rey Ducay, David W. Messinger

JARS, Vol. 17, Issue 02, 024504, (April 2023) https://doi.org/10.1117/12.10.1117/1.JRS.17.024504

KEYWORDS: Image fusion, Target detection, Detection and tracking algorithms, Image quality, Hyperspectral imaging, Education and training, Multispectral imaging, Image enhancement, Diffusion, Sensors

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SPIE Journal Paper | 23 March 2023

Image quality and computer vision performance: assessing the effects of image distortions and modeling performance relationships using the general image quality equation

Austin C. Bergstrom, David W. Messinger

JEI, Vol. 32, Issue 02, 023018, (March 2023) https://doi.org/10.1117/12.10.1117/1.JEI.32.2.023018

KEYWORDS: Image quality, Performance modeling, Education and training, Data modeling, Computer vision technology, Visual process modeling, Image classification, Image processing, Visualization, Image sensors

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Proceedings Article | 31 May 2022 Presentation + Paper

Graph convolutional network for automatic pigment clustering of cultural heritage artifacts

Bei Grace Cao, David Messinger

Proceedings Volume 12094, 120940U (2022) https://doi.org/10.1117/12.2618456

KEYWORDS: Convolution, RGB color model, Hyperspectral imaging, Data modeling, Cultural heritage, Performance modeling, Solid state lighting, Image classification, Statistical analysis, Fourier transforms

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Proceedings Article | 31 May 2022 Presentation + Paper

Hyperspectral-multispectral image fusion using nearest-neighbor diffusion-based sharpening algorithm

Rey Ducay, David Messinger

Proceedings Volume 12094, 120940M (2022) https://doi.org/10.1117/12.2619267

KEYWORDS: Image fusion, Detection and tracking algorithms, Hyperspectral imaging, Target detection, Sensors, Image sensors, Multispectral imaging, Spectral resolution, Image classification, Spatial resolution

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Proceedings Article | 27 May 2022 Presentation + Paper

Understanding the relationship between image quality and convolutional neural network performance

Austin Bergstrom, David Messinger

Proceedings Volume 12101, 1210103 (2022) https://doi.org/10.1117/12.2618590

KEYWORDS: Image quality, Performance modeling, Data modeling, Image resolution, Image information entropy, Image sensors, Image analysis, Sensors, Machine vision, Image classification

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Proceedings Article | 12 April 2021 Presentation + Paper

Automatic clustering of inks in cultural heritage artifacts via optimal selection of graph modularity

Bei Grace Cao, David Messinger

Proceedings Volume 11727, 117270Q (2021) https://doi.org/10.1117/12.2585726

KEYWORDS: Hyperspectral imaging, Visualization, Analytical research

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We present an automatic clustering algorithm for hyperspectral imagery of cultural heritage artifacts: the Selden Map and the Gough Map, both medieval artifacts imaged in the collections at the Bodleian Library of Oxford University. Unlike "traditional" remotely sensed hyperspectral data, these images are of man-made objects using specific materials meant to visually show feature differences and similarities. Consequently, the data are inherently non-Gaussian and potentially very non-linear in the spectral domain. First, we explore the effective graph representations for hyperspectral images, then optimally select the graph modularity to find community structures for a ROI within the scene. By utilizing the eigenvector of the modularity matrix associated with the largest positive eigenvalue for group labeling, we recursively identify multi-level subgroups existing in the graph, producing a variable level of detail cluster-map based on a cluster tree strategy. The generated non-linear decision boundaries are allowed to take any shape with no limits to cluster size. The clustering metric is determined by optimally selecting a high modularity and the largest positive eigenvalue, as well as considering the magnitude of the entry in the leading eigenvector to make each division more accurate and robust. As a result, the optimal number of clusters are found to best characterize the data. Compared to the traditional clustering methods, such as K-means, the graph modularity-based method can extract perceptually important non-local properties of an object, thus yielding semantically more meaningful cluster groups and better discriminating subtle spectral differences between varied pigments. For the Selden Map, we investigate subtle differences in black inks used to denote navigation routes. For the Gough Map, we look at a specific feature under investigation by historians: the castle depicting London. Our results demonstrate the effectiveness of the method as the clustering results explain and match the actual spectra well. This research aims to aid historians in analysis of pigment composition and further facilitate the study in inference of the creation and the evolving timeline of these artifacts.

Proceedings Article | 12 April 2021 Presentation + Paper

Subpixel target implantation to assess pansharpening performance on hyperspectral datasets

Rey Ducay, David Messinger

Proceedings Volume 11727, 117270Z (2021) https://doi.org/10.1117/12.2587335

KEYWORDS: Target detection, Image fusion, Detection and tracking algorithms, Hyperspectral imaging, Spectral resolution, Sensors, Image processing, Hyperspectral target detection, Data fusion, Image resolution

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Proceedings Article | 12 April 2021 Presentation + Paper

A simple web-based tool for multi-spectral surface visualization

Snehal Padhye, David Messinger, James Ferwerda

Proceedings Volume 11727, 1172716 (2021) https://doi.org/10.1117/12.2587799

KEYWORDS: Visualization, Computer graphics, 3D modeling, Stereoscopy, Remote sensing, Medical diagnostics, Human-machine interfaces, Astronomy, 3D visualizations

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Proceedings Article | 12 April 2021 Presentation + Paper

Multi-resolution hyperspectral collection for analysis of panchromatic sharpening algorithms

Joseph Carrock, David Messinger

Proceedings Volume 11727, 1172715 (2021) https://doi.org/10.1117/12.2587055

KEYWORDS: Spatial resolution, Imaging systems, Hyperspectral imaging, Sensors, RGB color model, Image resolution, Reflectivity, Calibration, Detection and tracking algorithms, Target detection

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Proceedings Article | 12 April 2021 Presentation + Paper

Low-cost, user friendly multispectral imaging system for the recovery of damaged, faded or palimpsested historical documents

Tania Kleynhans, MacKenzie Carr, David Messinger

Proceedings Volume 11727, 117270F (2021) https://doi.org/10.1117/12.2585629

KEYWORDS: Multispectral imaging, Imaging systems, Systems modeling, Data modeling, X-ray fluorescence spectroscopy, Spectroscopy, Prototyping, Performance modeling, Optical filtering, Linear filtering

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Proceedings Article | 19 May 2020 Presentation + Paper

Radiometric assessment of four pan-sharpening algorithms as applied to hyperspectral imagery

Rey Ducay, David Messinger

Proceedings Volume 11392, 1139203 (2020) https://doi.org/10.1117/12.2558741

KEYWORDS: Target detection, Detection and tracking algorithms, Hyperspectral imaging, Multispectral imaging, Spectral resolution, Sensors, Image fusion, RGB color model, Spatial resolution, Visualization

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Proceedings Article | 24 April 2020 Presentation + Paper

Automatic material classification of paintings in illuminated manuscripts from VNIR reflectance hyperspectral data cubes

Tania Kleynhans, John Delaney, David Messinger

Proceedings Volume 11392, 113920J (2020) https://doi.org/10.1117/12.2557890

KEYWORDS: Reflectivity, Algorithm development, Hyperspectral imaging, Remote sensing, Chemical analysis, Data acquisition, Image analysis, Analytical research

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Identification of the materials used in the creation of paintings and illuminated manuscripts can provide conservators with knowledge on how to better preserve objects, and supply scholars with information about the provenance of the objects. Significant headway has been made with non-invasive imaging techniques in the past decade. One example is hyperspectral imaging systems that were initially developed for the remote sensing community, and are now more widely applied to assist with art conservation. Although hardware, such as hyperspectral systems, are becoming more widely used, the software to analyze the acquired data is not yet well developed for pigment analysis of artworks. Pigment specific chemical information is derived from hyperspectral data, leveraging spectral signatures to separate out the various artists' materials used in the creation of the artwork. In general, methods of pigment analysis and mapping of paintings in illuminated manuscripts and artworks involve various algorithms and processing steps to create spatial distribution maps of the spectral signatures. When analyzing a particular work, the goals of the analysis are threefold: determine the number of unique pigments (not visual colors) used in the creation of the object; identify the unique pigment reflectance signatures from the data directly or from previously measured library spectra; and map the location and possible distribution of each pigment across the painting. This information is then passed on to conservators and art historians to inform their efforts. Current endmember selection methods involve significant user input, best done by an expert user and are time-consuming. This research examines the accuracy of creating abundance maps (pigment distribution maps) from automatically derived endmembers (using the Maximum Distance algorithm) to create useful information maps to inform conservators. The non-negative lease squares (NNLS) method is used to un-mix the data to create element distribution maps. Algorithms were evaluated with hyperspectral data of Cosmé Tura's Saint Francis Receiving the Stigma, in the collection of the National Gallery of Art, Washington, DC. Results compare well with what is known of the pigment used to create this painting.

Proceedings Article | 24 April 2020 Presentation + Paper

Hyperspectral analysis: black inks separation in the Gough map of Great Britain

Sihan Huang, David Messinger, Damien Bove

Proceedings Volume 11392, 113920K (2020) https://doi.org/10.1117/12.2558218

KEYWORDS: Target detection, Hyperspectral imaging

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The Gough Map is a late medieval map of the island of Great Britain, which has been receiving a lot of research attention and many studies have been conducted to analyze the map. With the hyperspectral image (HSI) of the Gough Map collected, we are allowed to further analysis the map in a different angle. Our goal is to semi-automatically identify and separate iron-gall and carbon black inks written on the Gough map. Unlike traditional target detection problems, there are unique characteristics in our data: Firstly, the targets are not sparsely distributed and their statistical contributions to the background estimation cannot be ignored. Secondly, the spectral differences between the targets are subtle. Lastly, the variances of the background data are so low that the distinction between it and noise is challenging. To address these issues, we made the following modification to the traditional adaptive coherence estimator (ACE): First, manually select the background data instead of using the whole data to estimate the background. Moreover, the pixels with strong spectral features of other pigments are removed and only the black ink candidates are feed into the detector. Thirdly, the number of eigenvectors were limited in the calculation of whitening operator so that the impact of noise can be controlled. It is clear that the trailing eigenvectors are usually dominated by the noise, so the majority of the information of background data can be effectively compressed only with the top-ranked eigenvectors. This is critical because whitening the noise means increasing the variance of the noise based on the fact that our background has super low variances. The whitening matrix needs to be used for whitening target candidates which have subtle spectral differences, and whitening the noise will cause the spectral features to disappear in the whitening sub-space. In conclusion, the whitening operator used in ACE should whiten the useful information of background data and reduce the effect caused by the noise as much as possible so that the subtle spectral differences of targets will not be buried in the whitening sub-space. Our results show that the modified target detection algorithm can both separate the targets from the background and each other.

Proceedings Article | 14 May 2019 Presentation + Paper

Hyperspectral pigment analysis of cultural heritage artifacts using the opaque form of Kubelka-Munk theory

Abu Md Niamul Taufique, David Messinger

Proceedings Volume 10986, 1098611 (2019) https://doi.org/10.1117/12.2518451

KEYWORDS: Cultural heritage, Opacity, Reflectivity, Library classification systems, Error analysis

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Proceedings Article | 13 May 2019 Presentation + Paper

Panchromatic sharpening enabling low-intensity imaging of cultural heritage documents

Tyler Peery, David Messinger

Proceedings Volume 10980, 1098004 (2019) https://doi.org/10.1117/12.2518242

KEYWORDS: Signal to noise ratio, Sensors, Image resolution, Spatial resolution, Cultural heritage, Image quality, Image processing, Target detection, Imaging systems, Principal component analysis, Hyperspectral imaging

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The spatial-spectral compromise inherent in hyperspectral imaging (HSI) refers to one dimension of an HSI detector being devoted to spectral information instead of a second spatial dimension. By dividing light into contiguous bands of energy, HSI systems decrease recorded photon count per pixel from a scene often by a factor of 100 compared to typical RGB or panchromatic imaging systems, and by one to two orders of magnitude for their multispectral imaging (MSI) counterparts. This proves particularly problematic when imaging cultural heritage objects which may be sensitive to damage from intense illumination. A method to counter the signal loss is to sacrifice spatial resolution, increasing a pixel's projected footprint on a target, thus increasing the number of photons striking the pixel per time interval. Panchromatic sharpening is then applied in this research to visually recover the decreased spatial resolution. The increase in ground sample distance (GSD) can be accomplished physically, by increasing the imaging distance. Though this method does not increase the signal itself, as the projected area of the detector does not change, it does allow for quicker scan times over a given area. This allows the user to increase integration time, resulting in a higher integrated signal recorded over similar total scan times than at higher resolutions or lower imaging heights. In this research, a 14th century manuscript was imaged with an HSI detector under museum lighting levels of 50 lux. Here we show that as the spatial resolution was digitally downsampled by factors of approximately two and four, from 333 pixels per inch (ppi) to 161 and 80 ppi, the signal-to-noise ratio (SNR) was effectively increased by a factor of 2.00 and 2.79. Even after being spatially downsampled, the sharpened images were up to 1.5 times spatially sharper than the reference HSI image. Relatively high spectral accuracy was also maintained, with spectral angle mapper (SAM) measurements of 0.0527-0.0963.

Proceedings Article | 18 September 2018 Presentation + Paper

MSI vs. HSI in cultural heritage imaging

Tyler Peery, David Messinger

Proceedings Volume 10768, 107680G (2018) https://doi.org/10.1117/12.2320671

KEYWORDS: Signal to noise ratio, Sensors, Imaging systems, Multispectral imaging, Light emitting diodes, Systems modeling, Signal detection, Human vision and color perception, Spatial resolution, Eye

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Proceedings Article | 8 May 2018 Paper

Processes for conducting HSI and MSI pan-sharpening with 3D digital flattening

Tyler Peery, David Messinger

Proceedings Volume 10644, 106441W (2018) https://doi.org/10.1117/12.2304375

KEYWORDS: Multispectral imaging, Image processing, Cameras, Sensors, 3D image processing, Distortion, 3D modeling, RGB color model, Spectral resolution

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Proceedings Article | 8 May 2018 Paper

Spectral phenomenology of historical parchments and inks to aid cultural heritage imaging system development

Tyler Kuhns, David Messinger

Proceedings Volume 10644, 1064420 (2018) https://doi.org/10.1117/12.2304812

KEYWORDS: Reflectivity, Imaging systems, Skin, Cultural heritage, Hyperspectral imaging, Spectroscopy, Short wave infrared radiation, Document imaging

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Proceedings Article | 8 May 2018 Presentation + Paper

Pigment diversity estimation for hyperspectral images of the Selden map of China

Di Bai, David Messinger, David Howell

Proceedings Volume 10644, 1064415 (2018) https://doi.org/10.1117/12.2304041

KEYWORDS: Hyperspectral imaging, Image classification, Analytical research, Image analysis, Imaging systems, Cultural heritage, Reflectivity, Visualization, Binary data, RGB color model

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SPIE Journal Paper | 23 March 2018

Joint spatial–spectral hyperspectral image clustering using block-diagonal amplified affinity matrix

Lei Fan, David Messinger

OE, Vol. 57, Issue 03, 033107, (March 2018) https://doi.org/10.1117/12.10.1117/1.OE.57.3.033107

KEYWORDS: Electroactive polymers, Data modeling, Hyperspectral imaging, Image filtering, Image processing, Composites, Matrices, Optical engineering, Statistical modeling, Principal component analysis

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SPIE Journal Paper | 7 March 2018

Bloodstain detection and discrimination impacted by spectral shift when using an interference filter-based visible and near-infrared multispectral crime scene imaging system

Jie Yang, David W. Messinger, Roger R. Dube

OE, Vol. 57, Issue 03, 033101, (March 2018) https://doi.org/10.1117/12.10.1117/1.OE.57.3.033101

KEYWORDS: Transmittance, Multispectral imaging, Iterated function systems, Imaging systems, Blood, Optical filters, Calibration, Absorbance, Forensic science, Visible radiation

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SPIE Journal Paper | 15 December 2017

Remotely sensed physical property estimation from powder contaminated surfaces

Timothy Gibbs, David Messinger

JARS, Vol. 11, Issue 04, 046021, (December 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.046021

KEYWORDS: Particles, Data modeling, Sensors, Reflectivity, Silica, Long wavelength infrared, Silicon carbide, Contamination, Temperature metrology, Error analysis

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SPIE Journal Paper | 9 September 2017

Spatial–spectral Schroedinger embedding for target detection in hyperspectral imagery

Leidy Dorado-Munoz, David Messinger

OE, Vol. 56, Issue 09, 093101, (September 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.9.093101

KEYWORDS: Target detection, Sensors, Optical engineering, Hyperspectral target detection, Hyperspectral imaging, Binary data, Data modeling, RGB color model, Detection and tracking algorithms, Selenium

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SPIE Journal Paper | 2 June 2017

Hyperspectral analysis of cultural heritage artifacts: pigment material diversity in the Gough Map of Britain

Di Bai, David Messinger, David Howell

OE, Vol. 56, Issue 08, 081805, (June 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.8.081805

KEYWORDS: Analytical research, Cultural heritage, Hyperspectral imaging, Image enhancement, Visualization, Imaging systems, Visual analytics

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Proceedings Article | 5 May 2017 Paper

Contaminant mass estimation of powder contaminated surfaces

Timothy Gibbs, David Messinger

Proceedings Volume 10198, 101980M (2017) https://doi.org/10.1117/12.2262584

KEYWORDS: Long wavelength infrared, Infrared signatures, Data modeling, Contamination, Particles, Infrared radiation, Systems modeling, Databases, FT-IR spectroscopy, Solids, Scattering, Reflectivity, Sensors, Temperature metrology, Silicon carbide

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Proceedings Article | 5 May 2017 Presentation + Paper

On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery

Wei Yao, Jan van Aardt, David Messinger

Proceedings Volume 10198, 1019814 (2017) https://doi.org/10.1117/12.2262521

KEYWORDS: Lawrencium, Visible radiation, Data fusion, Spatial resolution, Image fusion, Imaging spectroscopy, Spectroscopy, Hyperspectral imaging, Ecosystems

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The Hyperspectral Infrared Imager (HyspIRI) mission aims to provide global imaging spectroscopy data to the benefit of especially ecosystem studies. The onboard spectrometer will collect radiance spectra from the visible to short wave infrared (VSWIR) regions (400-2500 nm). The mission calls for fine spectral resolution (10 nm band width) and as such will enable scientists to perform material characterization, species classification, and even sub-pixel mapping. However, the global coverage requirement results in a relatively low spatial resolution (GSD 30m), which restricts applications to objects of similar scales. We therefore have focused on the assessment of sub-pixel vegetation structure from spectroscopy data in past studies. In this study, we investigate the development or reconstruction of higher spatial resolution imaging spectroscopy data via fusion of multi-temporal data sets to address the drawbacks implicit in low spatial resolution imagery.

The projected temporal resolution of the HyspIRI VSWIR instrument is 15 days, which implies that we have access to as many as six data sets for an area over the course of a growth season. Previous studies have shown that select vegetation structural parameters, e.g., leaf area index (LAI) and gross ecosystem production (GEP), are relatively constant in summer and winter for temperate forests; we therefore consider the data sets collected in summer to be from a similar, stable forest structure. The first step, prior to fusion, involves registration of the multi-temporal data. A data fusion algorithm then can be applied to the pre-processed data sets. The approach hinges on an algorithm that has been widely applied to fuse RGB images. Ideally, if we have four images of a scene which all meet the following requirements - i) they are captured with the same camera configurations; ii) the pixel size of each image is x; and iii) at least r² images are aligned on a grid of x/r - then a high-resolution image, with a pixel size of x/r, can be reconstructed from the multi-temporal set.

The algorithm was applied to data from NASA's classic Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-C; GSD 18m), collected between 2013-2015 (summer and fall) over our study area (NEON's Southwest Pacific Domain; Fresno, CA) to generate higher spatial resolution imagery (GSD 9m). The reconstructed data set was validated via comparison to NEON's imaging spectrometer (NIS) data (GSD 1m). The results showed that algorithm worked well with the AVIRIS-C data and could be applied to the HyspIRI data.

Proceedings Article | 5 May 2017 Presentation + Paper

A pigment analysis tool for hyperspectral images of cultural heritage artifacts

Di Bai, David Messinger, David Howell

Proceedings Volume 10198, 101981A (2017) https://doi.org/10.1117/12.2261852

KEYWORDS: Analytical research, Hyperspectral imaging, Visualization, Library classification systems, Principal component analysis, Image analysis, Cultural heritage, Statistical analysis, Visual analytics, Mahalanobis distance, Image classification, RGB color model, Reflectivity, Optical spheres, Imaging systems, Image enhancement

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Proceedings Article | 5 May 2017 Presentation + Paper

Fixed Pattern Noise pixel-wise linear correction for crime scene imaging CMOS sensor

Jie Yang, David Messinger, Roger Dube, Emmett Ientilucci

Proceedings Volume 10198, 1019802 (2017) https://doi.org/10.1117/12.2262035

KEYWORDS: Image sensors, Image enhancement, Digital imaging, Interference (communication), Error analysis, Iterated function systems, Calibration, Imaging systems, CMOS sensors, Sensors, Cameras, Target detection, Optical sensors, Optical filters

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Proceedings Article | 26 October 2016 Presentation + Paper

Pollutant monitoring of aircraft exhaust with multispectral imaging

Emily Berkson, David Messinger

Proceedings Volume 10008, 1000803 (2016) https://doi.org/10.1117/12.2242070

KEYWORDS: Imaging systems, Pollution, Cameras, Multispectral imaging, Optical filters, Absorption, 3D modeling, Sensors, Long wavelength infrared, Electronic filtering

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SPIE Journal Paper | 2 August 2016

Initial study of Schroedinger eigenmaps for spectral target detection

Leidy Dorado-Munoz, David Messinger

OE, Vol. 55, Issue 08, 083101, (August 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.8.083101

KEYWORDS: Target detection, Sensors, Data modeling, Optical engineering, Detection and tracking algorithms, Binary data, Hyperspectral imaging, Statistical analysis, Statistical modeling, Hyperspectral target detection

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Proceedings Article | 17 May 2016 Paper

Spectral feature characterization methods for blood stain detection in crime scene backgrounds

Jie Yang, Jobin Mathew, Roger Dube, David Messinger

Proceedings Volume 9840, 98400E (2016) https://doi.org/10.1117/12.2224099

KEYWORDS: Blood, Forensic science, Near infrared, Absorption, Liquids, Bidirectional reflectance transmission function, Hyperspectral imaging, Target detection, Spectroscopy, Cameras

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Blood stains are one of the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest speciﬁcs about the nature of the violence that transpired at the scene. Blood spectral signatures containing unique reﬂectance or absorption features are important both for forensic on-site investigation and laboratory testing. They can be used for target detection and identiﬁcation applied to crime scene hyperspectral imagery, and also be utilized to analyze the spectral variation of blood on various backgrounds. Non-blood stains often mislead the detection and can generate false alarms at a real crime scene, especially for dark and red backgrounds. This paper measured the reﬂectance of liquid blood and 9 kinds of non-blood samples in the range of 350 nm - 2500 nm in various crime scene backgrounds, such as pure samples contained in petri dish with various thicknesses, mixed samples with diﬀerent colors and materials of fabrics, and mixed samples with wood, all of which are examined to provide sub-visual evidence for detecting and recognizing blood from non-blood samples in a realistic crime scene. The spectral diﬀerence between blood and non-blood samples are examined and spectral features such as “peaks” and “depths” of reﬂectance are selected. Two blood stain detection methods are proposed in this paper. The ﬁrst method uses index to denote the ratio of “depth” minus “peak” over“depth” add“peak” within a wavelength range of the reﬂectance spectrum. The second method uses relative band depth of the selected wavelength ranges of the reﬂectance spectrum. Results show that the index method is able to discriminate blood from non-blood samples in most tested crime scene backgrounds, but is not able to detect it from black felt. Whereas the relative band depth method is able to discriminate blood from non-blood samples on all of the tested background material types and colors.

Proceedings Article | 17 May 2016 Paper

Biased normalized cuts for target detection in hyperspectral imagery

Xuewen Zhang, Leidy Dorado-Munoz, David Messinger, Nathan Cahill

Proceedings Volume 9840, 98400Y (2016) https://doi.org/10.1117/12.2224067

KEYWORDS: Target detection, Hyperspectral target detection, Detection and tracking algorithms, Hyperspectral imaging, RGB color model, Image segmentation, Control systems, Silver, Principal component analysis, Algorithm development

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Proceedings Article | 17 May 2016 Paper

Tensor subspace analysis for spatial-spectral classification of hyperspectral data

Lei Fan, David Messinger

Proceedings Volume 9840, 984004 (2016) https://doi.org/10.1117/12.2223815

KEYWORDS: Data acquisition, Feature extraction, Sensors, Principal component analysis, Image processing, Image filtering, Matrices, Data modeling, Image classification, Targeting Task Performance metric

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Proceedings Article | 17 May 2016 Paper

Anomaly detection in hyperspectral imagery: statistics vs. graph-based algorithms

Emily Berkson, David Messinger

Proceedings Volume 9840, 98400V (2016) https://doi.org/10.1117/12.2222577

KEYWORDS: Hyperspectral imaging, Detection and tracking algorithms, Algorithms, Sensors, Image compression, Solids, Mahalanobis distance, Image segmentation, Distance measurement, RGB color model

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Proceedings Article | 17 May 2016 Paper

NEFDS contamination model parameter estimation of powder contaminated surfaces

Timothy Gibbs, David Messinger

Proceedings Volume 9840, 98400L (2016) https://doi.org/10.1117/12.2222302

KEYWORDS: Data modeling, Contamination, Particles, Quartz, Aluminum, Temperature metrology, Atmospheric particles, Long wavelength infrared, Optimization (mathematics), Sensors

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Proceedings Article | 17 May 2016 Paper

Comparison of algorithms for blood stain detection applied to forensic hyperspectral imagery

Jie Yang, David Messinger, Jobin Mathew, Roger Dube

Proceedings Volume 9840, 98400X (2016) https://doi.org/10.1117/12.2223293

KEYWORDS: Hyperspectral imaging, Detection and tracking algorithms, Blood, Forensic science, Principal component analysis, Imaging systems, Scene simulation, Cameras, RGB color model, Visualization

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Proceedings Article | 17 May 2016 Paper

Schroedinger eigenmaps with knowledge propagation for target detection

Leidy Dorado-Munoz, David Messinger

Proceedings Volume 9840, 984012 (2016) https://doi.org/10.1117/12.2223326

KEYWORDS: Target detection, Hyperspectral imaging, Detection and tracking algorithms, Binary data, Sensors, RGB color model, Selenium, Electroluminescent displays, Neodymium, Visible radiation

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Proceedings Article | 9 May 2016 Paper

Multispectral imaging of aircraft exhaust

Emily Berkson, David Messinger

Proceedings Volume 9862, 98620D (2016) https://doi.org/10.1117/12.2222604

KEYWORDS: Imaging systems, Multispectral imaging, Sensors, Prototyping, Pollution, Optical filters, Cameras, Vegetation, Data modeling, Absorption

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Proceedings Article | 15 October 2015 Paper

A game-theoretic tree matching approach for object detection in high-resolution remotely sensed images

Yilong Liang, Nathan Cahill, Eli Saber, David Messinger

Proceedings Volume 9643, 964315 (2015) https://doi.org/10.1117/12.2195178

KEYWORDS: Image segmentation, Detection and tracking algorithms, Sensors, Image processing algorithms and systems, Algorithms, Image resolution, Shape analysis, Image processing, Remote sensing, Feature extraction

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Proceedings Article | 15 October 2015 Paper

Low-dimensional representations of hyperspectral data for use in CRF-based classification

Yang Hu, Nathan Cahill, Sildomar Monteiro, Eli Saber, David Messinger

Proceedings Volume 9643, 96430L (2015) https://doi.org/10.1117/12.2195229

KEYWORDS: Microelectronics, Hyperspectral imaging, Data modeling, Image classification, Image processing, Mathematical modeling, Algorithm development, Computer programming, Image compression, Data centers

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Proceedings Article | 22 May 2015 Paper

Streaming analysis of track data from video

Jiangqin Sun, David Messinger

Proceedings Volume 9473, 947302 (2015) https://doi.org/10.1117/12.2180132

KEYWORDS: Probability theory, Video, Information science, Data centers, Visualization, Cameras, Data modeling, Sun, Phase modulation, Feature extraction

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A vast amount of temporal information are collected and stored which leads to a need for techniques to extract useful high-level information from the data, such as recognizable activities or events. This paper proposes a framework for streaming analysis of time series data, in this case track data from video sequences, which can recognize events without supervision and memorize them by building temporal contexts. The memorized historical data are then used to predict the future and detect anomalous activities or events. An incremental clustering method is used to recognize and learn the event without training. A memorization method of double localization, including relative and absolute localization, is proposed to model the temporal context. The first- order Markov chain is used as an example to relatively localize an event in the temporal space, while a temporal map is used to absolutely localize an event. By setting proper coordinates for the temporal map, prior temporal patterns can be included before building more delicate models to find subtle patterns. Finally, the predictive model is built based on the method of memorization. The "Edinburgh Informatics Forum Pedestrian Data Set", which offers about 1000 observed trajectories of pedestrians detected in camera images each working day for several months, is used as an example to illustrate the framework. The trajectories are incrementally recognized as activities, and the activities are further used to describe the situation (event) in the scene. By using the proposed framework, we not only get current high level information of the scene, such as activities and events that occur in the scene, but we can also reason whether current activities or events are "normal" by referring to the temporal context learned from historical data. For the data considered here, a total of 92,000+ observed trajectories and their temporal patterns over one year can be summarized by a temporal map of 16 events and a probability (transition) matrix between them. Our proposed framework offers an effective method of extracting useful and manageable information from a huge amount of raw trajectory data.

Proceedings Article | 21 May 2015 Paper

Schrodinger Eigenmaps for spectral target detection

Leidy Dorado-Munoz, David Messinger

Proceedings Volume 9472, 947211 (2015) https://doi.org/10.1117/12.2177302

KEYWORDS: Target detection, Selenium, Sensors, Hyperspectral imaging, Hyperspectral target detection, Data modeling, Binary data, Detection and tracking algorithms, Image segmentation, Visualization

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Proceedings Article | 21 May 2015 Paper

An adaptive locally linear embedding manifold learning approach for hyperspectral target detection

Amanda Ziemann, David Messinger

Proceedings Volume 9472, 94720O (2015) https://doi.org/10.1117/12.2177466

KEYWORDS: Target detection, Data modeling, Clouds, Hyperspectral target detection, Hyperspectral imaging, Sensors, Detection and tracking algorithms, RGB color model, Image sensors, Image analysis

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SPIE Journal Paper | 31 March 2015

Spatial segmentation of multi/hyperspectral imagery by fusion of spectral-gradient-textural attributes

Sreenath Rao Vantaram, Sankaranarayanan Piramanayagam, Eli Saber, David Messinger

JARS, Vol. 9, Issue 01, 095086, (March 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.095086

KEYWORDS: Image segmentation, Image fusion, Image processing algorithms and systems, Algorithm development, Hyperspectral imaging, Image classification, Image processing, Barium, Remote sensing, Matrices

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Proceedings Article | 27 February 2015 Paper

Shot boundary detection and label propagation for spatio-temporal video segmentation

Sankaranaryanan Piramanayagam, Eli Saber, Nathan Cahill, David Messinger

Proceedings Volume 9405, 94050D (2015) https://doi.org/10.1117/12.2076661

KEYWORDS: Image segmentation, Video, Detection and tracking algorithms, Algorithm development, 3D video streaming, Video processing, Image processing, Precision measurement, Databases, Cameras

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Proceedings Article | 27 February 2015 Paper

Classification of hyperspectral images based on conditional random fields

Yang Hu, Eli Saber, Sildomar Monteiro, Nathan Cahill, David Messinger

Proceedings Volume 9405, 940510 (2015) https://doi.org/10.1117/12.2083374

KEYWORDS: Hyperspectral imaging, Data modeling, RGB color model, Feature selection, Image classification, Image analysis, Image resolution, Sensors, Tolerancing, Microelectronics

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Proceedings Article | 19 June 2014 Paper

Multimodal sensor experiment for evaluating motion imagery in activity-based intelligence

Christian Lewis, David Messinger, Briana Neuberger

Proceedings Volume 9089, 90890P (2014) https://doi.org/10.1117/12.2050539

KEYWORDS: Sensors, Polarimetry, Video, Detection and tracking algorithms, Light emitting diodes, RGB color model, Diodes, Thermography, Target detection, Cameras

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Proceedings Article | 13 June 2014 Paper

Hyperspectral target detection using graph theory models and manifold geometry via an adaptive implementation of locally linear embedding

Amanda Ziemann, David Messinger

Proceedings Volume 9088, 90880B (2014) https://doi.org/10.1117/12.2050382

KEYWORDS: Target detection, Data modeling, Hyperspectral imaging, Clouds, Hyperspectral target detection, Detection and tracking algorithms, Sensors, Image analysis, Statistical modeling, Remote sensing

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Proceedings Article | 13 June 2014 Paper

Graph based hyperspectral image segmentation with improved affinity matrix

Lei Fan, David Messinger

Proceedings Volume 9088, 908802 (2014) https://doi.org/10.1117/12.2050397

KEYWORDS: Image segmentation, Hyperspectral imaging, Data modeling, RGB color model, Image processing algorithms and systems, Sensors, Visualization, Statistical modeling, Matrices, Buildings

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Proceedings Article | 13 June 2014 Paper

Assessment of Schrodinger Eigenmaps for target detection

Leidy Dorado Munoz, David Messinger, Wojtek Czaja

Proceedings Volume 9088, 908813 (2014) https://doi.org/10.1117/12.2050387

KEYWORDS: Target detection, Roads, Hyperspectral imaging, Hyperspectral target detection, Image processing, Detection and tracking algorithms, Computer programming, Principal component analysis, Visualization, Selenium

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Proceedings Article | 13 June 2014 Paper

Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery

Nathan Cahill, Wojciech Czaja, David Messinger

Proceedings Volume 9088, 908804 (2014) https://doi.org/10.1117/12.2050651

KEYWORDS: Hyperspectral imaging, Image classification, Image fusion, Matrices, Spatial analysis, Distance measurement, Neptunium, Data fusion, Image segmentation, Spatial resolution

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Proceedings Article | 9 June 2014 Paper

Estimating sampling completeness of lidar datasets using voxel-based geometry

Shea Hagstrom, David Messinger, Katie Salvaggio

Proceedings Volume 9080, 90800P (2014) https://doi.org/10.1117/12.2050552

KEYWORDS: LIDAR, Interfaces, Data modeling, 3D modeling, Clouds, Pulsed laser operation, Tin, Statistical analysis, Buildings, Solids

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Proceedings Article | 21 May 2014 Paper

Activity-based intelligence tipping and cueing using polarimetric sensors

Christian Lewis, David Messinger, Michael Gartley

Proceedings Volume 9099, 90990C (2014) https://doi.org/10.1117/12.2050743

KEYWORDS: Sensors, Polarimetry, Video, Polarization, Detection and tracking algorithms, Intelligent sensors, Light emitting diodes, Diodes, RGB color model, Video processing

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SPIE Journal Paper | 24 January 2014 Open Access

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

Weihua Sun, Bin Chen, David Messinger

OE, Vol. 53, Issue 01, 013107, (January 2014) https://doi.org/10.1117/12.10.1117/1.OE.53.1.013107

KEYWORDS: Multispectral imaging, Diffusion, RGB color model, Image resolution, Optical engineering, Visualization, Sensors, Error analysis, Spectral resolution, Image processing

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Proceedings Article | 7 August 2013 Paper

Multimodal scenario analysis and visualization tool

Erin Ontiveros, Rolando Raqueno, Andrew Scott, Chris DeAngelis, Dave Messinger

Proceedings Volume 8758, 87580P (2013) https://doi.org/10.1117/12.2015309

KEYWORDS: Visualization, Image visualization, Visual analytics, Process modeling, Thermal modeling, Image quality, Prototyping, Visual process modeling, Chemical analysis, Digital imaging

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Proceedings Article | 20 May 2013 Paper

Fusing LIDAR-based voxel geometry with multi-angle visible imagery

Shea Hagstrom, David Messinger

Proceedings Volume 8731, 873109 (2013) https://doi.org/10.1117/12.2015156

KEYWORDS: 3D modeling, LIDAR, Tin, Image fusion, Data modeling, RGB color model, Opacity, 3D image processing, Process modeling, Statistical modeling

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