Dr. Amanda K. Ziemann Profile

Dr. Amanda K. Ziemann

Scientist at Los Alamos National Lab

SPIE Involvement:

Conference Program Committee | Editorial Board Member: Optical Engineering | Author

Area of Expertise:

remote sensing

Publications (26)

Proceedings Article | 4 October 2023 Presentation + Paper

Physics-guided neural networks for hyperspectral target identification

Natalie Klein, Adra Carr, Zigfried Hampel-Arias, Amanda Ziemann, Eric Flynn

Proceedings Volume 12675, 1267503 (2023) https://doi.org/10.1117/12.2684140

KEYWORDS: Hyperspectral imaging, Hyperspectral target detection, Emissivity, Physics, Neural networks, Data modeling, Atmospheric modeling, Long wavelength infrared, Air temperature, Target recognition

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Proceedings Article | 13 June 2023 Presentation + Paper

Experiments in anomalous change detection: improving detector discrimination through feature layers

Amanda Ziemann, Zigfried Hampel-Arias

Proceedings Volume 12519, 125190P (2023) https://doi.org/10.1117/12.2666569

KEYWORDS: Remote sensing, Image information entropy

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Proceedings Article | 31 May 2022 Presentation + Paper

Quantifying uncertainty in machine learning for hyperspectral target detection and identification

Eric Flynn, Natalie Klein, Adra Carr, Zigfried Hampel-Arias, Amanda Ziemann, Kevin Mitchell

Proceedings Volume 12094, 120940B (2022) https://doi.org/10.1117/12.2622926

KEYWORDS: Neural networks, Data modeling, Target detection, Statistical modeling, Hyperspectral target detection, Machine learning, Monte Carlo methods, Long wavelength infrared, Gases, Target recognition

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SPIE Journal Paper | 14 September 2021

Multi-sensor anomalous change detection in remote sensing imagery

Amanda Ziemann, Christopher Ren, James Theiler

JARS, Vol. 15, Issue 04, 042411, (September 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.042411

KEYWORDS: Multispectral imaging, Sensors, Synthetic aperture radar, Remote sensing, Earth observing sensors, Landsat, Satellites, Satellite imaging, Image sensors, Vegetation

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Proceedings Article | 23 April 2021 Presentation + Paper

Deepfaking it: experiments in generative, adversarial multispectral remote sensing

Christopher Ren, Juston Moore, Amanda Ziemann, James Theiler

Proceedings Volume 11727, 117270M (2021) https://doi.org/10.1117/12.2587753

KEYWORDS: Remote sensing, Video, Gallium nitride, Data modeling, Virtual reality, Silicon, Satellites, Satellite imaging, Neural networks, Motion models

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Proceedings Article | 20 April 2021 Poster + Presentation + Paper

Improving mosquito population models over the Greater Toronto Area using MSI and SAR data

Sydney DeMets, Amanda Ziemann, Curtis Russell, Carrie Manore

Proceedings Volume 11727, 117271I (2021) https://doi.org/10.1117/12.2587714

KEYWORDS: Synthetic aperture radar, Multispectral imaging, Vegetation, Landsat, Earth observing sensors, Systems modeling, Spatial resolution, Backscatter

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As West Nile Virus (WNV) and St. Louis Encephalitis (SLE) become more prevalent across North America, there is an increased risk of fatal neuroinvasive cases. In order for public health officials to prepare for these cases and potentially intervene, the ability to forecast mosquito borne disease outbreaks is paramount. In practice, however, such vector borne diseases are notoriously difficult to predict due to their seemingly sporadic spatial and temporal outbreak patterns. Recent research has demonstrated that mosquito abundance is causally related to WNV/SLE prevalence, providing a practical starting point for developing mosquito-borne disease forecasting systems. When focusing on building mosquito population models, understanding the reproduction environment of Culex mosquitos (WNV and SLE's primary vectors) is key: they rely on warmth, water, and vegetation to reproduce. Previous work has shown that global-coverage multispectral imagery (MSI) (i.e., Landsat 8, Sentinel- 2) is a valuable resource for characterizing vegetation health as a predictor of mosquito population, but it is limited in that it may not provide the spatial resolution necessary to distinguish between, e.g., a well-fertilized lawn (poor Culex habitat) and a stand of trees (good Culex habitat). The backscatter information collected by synthetic aperture radar (SAR) imagery provides opportunity to distinguish between broader categories of vegetation type, potentially helping to fill this gap. This research uses publicly available global-coverage MSI and SAR imagery (Landsat 8, Sentinel-2, and Sentinel-1) to explore if vegetation type, in tandem with vegetation health, improves our ability to forecast mosquito populations. Vegetation characterization is done over the Greater Toronto Area from 2014 to 2017, and we derive weekly time series from MSI, spectral indices, and SAR for this time period. We then quantify the strength of vegetation health and type as a predictor of Culex abundance.

Proceedings Article | 12 April 2021 Presentation + Paper

Remote sensing of polar ice: combining synthetic aperture radar and machine learning for operational navigability

Elena Reinisch, Christopher Ren, Andrew Roberts, Cathy Wilson, Amanda Ziemann, Patrick Eriksson

Proceedings Volume 11728, 1172806 (2021) https://doi.org/10.1117/12.2587518

KEYWORDS: Synthetic aperture radar, Machine learning, Remote sensing, Lead, Data modeling, Spatial resolution, Satellites, Ridge detection, Image analysis, Climatology

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Proceedings Article | 19 May 2020 Open Access

Too big, too small, or just right? The influence of multispectral image size on mosquito population predictions in the greater Toronto area

Sydney DeMets, Amanda Ziemann, Carrie Manore, Curtis Russell

Proceedings Volume 11392, 113920Y (2020) https://doi.org/10.1117/12.2558128

KEYWORDS: Data modeling, Multispectral imaging, Near infrared, Vegetation, Model-based design, Infrared imaging, Earth observing sensors, Landsat, Animal model studies, Temperature metrology

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Proceedings Article | 19 May 2020 Paper

Investigating temporal distributions for spectral anomaly detection through time

Hope Simonoko, Amanda Ziemann, Eric Flynn

Proceedings Volume 11392, 113920X (2020) https://doi.org/10.1117/12.2557894

KEYWORDS: Multispectral imaging, Detection and tracking algorithms, Mahalanobis distance, Composites, Sensors, Statistical analysis, Vector spaces

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Proceedings Article | 19 May 2020 Presentation + Paper

Experiments in anomalous change detection with the Viareggio 2013 trial dataset

James Theiler, Michal Kucer, Amanda Ziemann

Proceedings Volume 11392, 1139211 (2020) https://doi.org/10.1117/12.2564106

KEYWORDS: Detection and tracking algorithms, Target detection, Sensors, Canonical correlation analysis, Hyperspectral imaging, Algorithm development, Machine learning, Dimension reduction

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Proceedings Article | 19 May 2020 Paper

Deep snow: synthesizing remote sensing imagery with generative adversarial nets

Christopher Ren, Alice M. Durieux, Amanda Ziemann, James Theiler

Proceedings Volume 11392, 113920T (2020) https://doi.org/10.1117/12.2560716

KEYWORDS: Remote sensing, RGB color model, Image quality, Clouds, Image processing, Data modeling, Neural networks, Visualization, Machine learning

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Proceedings Article | 15 May 2020 Paper

Combining multispectral imagery and synthetic aperture radar for detecting deforestation

Elena Reinisch, Amanda Ziemann, Eric Flynn, James Theiler

Proceedings Volume 11392, 1139209 (2020) https://doi.org/10.1117/12.2558201

KEYWORDS: Synthetic aperture radar, Multispectral imaging, Vegetation, Interferometric synthetic aperture radar, Interferometry, Phase interferometry

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Forest destruction is a main contributor to carbon emissions and loss of biodiversity, making it a matter of global importance. Due to the large global footprint and often inaccessibility of forested areas, remote sensing is one of the most valuable techniques for monitoring deforestation. Spectral imaging is typically favored for material classification of forested areas and identification of broad swaths of deforestation. However, spectral data can fall short in detecting more subtle destruction beneath the forest canopy. Radar remote sensing can help fill this gap, as it has the ability to penetrate through tree canopies such that pixels capture backscatter information from both the canopy and material beneath it. Synthetic aperture radar in particular can capture this information at fine spatial resolution, and techniques such as polarimetry and interferometry can be used to measure biomass and detect deforestation. In this study, we compare synthetic aperture radar data with multispectral data to improve characterization and identification of source signatures captured within a pixel, with specific consideration to detecting areas where thinning is happening beneath the forest canopy. We focus on identifying different types of forest thinning in the Valles Caldera, located in the Jemez Mountains of northern New Mexico. We apply anomalous change detection to a combination of data products derived from multispectral imagery and synthetic aperture radar to determine which combinations are most effective at identifying anomalous features of interest in thinning regions. We find that comparing phase change measured by synthetic aperture radar interferometry to differenced vegetation indices highlights anomalous relationships in the thinning region. When comparing multispectral reflectance to backscatter intensity measured by synthetic aperture radar, the most successful temporal comparisons contained synthetic aperture radar data during the thinning period. This suggests that synthetic aperture radar enhances detection of thinning practices via remote sensing, especially in regards to changes taking place beneath the tree canopy. These results were improved even further by segmenting the images according to vegetation coverage prior to applying anomalous change detection techniques.

Proceedings Article | 14 May 2019 Paper

Multi-sensor anomalous change detection at scale

Amanda Ziemann, James Theiler, Christopher Ren

Proceedings Volume 10986, 1098615 (2019) https://doi.org/10.1117/12.2519167

KEYWORDS: Multispectral imaging, Synthetic aperture radar, Landsat, Remote sensing

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Proceedings Article | 14 May 2019 Paper

Understanding polynomial distributed lag models: truncation lag implications for a mosquito-borne disease risk model in Brazil

Jessica Conrad, Randall Refeld, Nidhi Parikh, Amir Siraj, Nicholas Generous, Sara Del Valle, Geoffrey Fairchild, Carrie Manore, Amanda Ziemann

Proceedings Volume 10986, 109860N (2019) https://doi.org/10.1117/12.2536369

KEYWORDS: Data modeling, Vegetation, Humidity, Landsat, Statistical analysis, Remote sensing, Satellites, Data acquisition

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Proceedings Article | 8 May 2018 Paper

A machine learning approach to hyperspectral detection of solid targets

Amanda Ziemann, James Theiler, Michal Kucer

Proceedings Volume 10644, 1064404 (2018) https://doi.org/10.1117/12.2305232

KEYWORDS: Target detection, Hyperspectral target detection, Machine learning, Detection and tracking algorithms, Hyperspectral imaging, Solids

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SPIE Journal Paper | 15 June 2017

Simplex ACE: a constrained subspace detector

Amanda Ziemann, James Theiler

OE, Vol. 56, Issue 08, 081808, (June 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.8.081808

KEYWORDS: Sensors, Hyperspectral target detection, Target detection, Detection and tracking algorithms, Digital filtering, Hyperspectral imaging

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Proceedings Article | 5 May 2017 Presentation + Paper

Local background estimation and the replacement target model

James Theiler, Amanda Ziemann

Proceedings Volume 10198, 101980V (2017) https://doi.org/10.1117/12.2262833

KEYWORDS: RGB color model, Solids, Contamination, Target detection, Digital filtering, Gaussian filters, Sensors, Hyperspectral target detection, Image analysis, Hyperspectral imaging, Multispectral imaging, Signal detection, Performance modeling

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Proceedings Article | 5 May 2017 Paper

Experiments with Simplex ACE: dealing with highly variable targets

Amanda Ziemann, James Theiler, Emmett Ientilucci

Proceedings Volume 10198, 101980F (2017) https://doi.org/10.1117/12.2262031

KEYWORDS: Picosecond phenomena, RGB color model, Digital filtering, Chemical elements, Reflectivity, Target detection, Hyperspectral target detection, Sensors, Detection and tracking algorithms, Solids, Hyperspectral imaging

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Proceedings Article | 17 May 2016 Paper

Graph-based and statistical approaches for detecting spectrally variable target materials

Amanda Ziemann, James Theiler

Proceedings Volume 9840, 98400T (2016) https://doi.org/10.1117/12.2224091

KEYWORDS: Target detection, Hyperspectral target detection, Hyperspectral imaging, Detection and tracking algorithms, Solids, Particles, Sensors, Signal processing, Chemical analysis, RGB color model

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In discriminating target materials from background clutter in hyperspectral imagery, one must contend with variability in both. Most algorithms focus on the clutter variability, but for some materials there is considerable variability in the spectral signatures of the target. This is especially the case for solid target materials, whose signatures depend on morphological properties (particle size, packing density, etc.) that are rarely known a priori. In this paper, we investigate detection algorithms that explicitly take into account the diversity of signatures for a given target. In particular, we investigate variable target detectors when applied to new representations of the hyperspectral data: a manifold learning based approach, and a residual based approach. The graph theory and manifold learning based approach incorporates multiple spectral signatures of the target material of interest; this is built upon previous work that used a single target spectrum. In this approach, we first build an adaptive nearest neighbors (ANN) graph on the data and target spectra, and use a biased locally linear embedding (LLE) transformation to perform nonlinear dimensionality reduction. This biased transformation results in a lower-dimensional representation of the data that better separates the targets from the background. The residual approach uses an annulus based computation to represent each pixel after an estimate of the local background is removed, which suppresses local backgrounds and emphasizes the target-containing pixels. We will show detection results in the original spectral space, the dimensionality-reduced space, and the residual space, all using subspace detectors: ranked spectral angle mapper (rSAM), subspace adaptive matched filter (ssAMF), and subspace adaptive cosine/coherence estimator (ssACE). Results of this exploratory study will be shown on a ground-truthed hyperspectral image with variable target spectra and both full and mixed pixel targets.

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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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 | 18 May 2013 Paper

Target detection performed on manifold approximations recovered from hyperspectral data

Amanda Ziemann, David Messinger, James Albano

Proceedings Volume 8743, 874319 (2013) https://doi.org/10.1117/12.2015780

KEYWORDS: Target detection, Data modeling, Roads, Hyperspectral imaging, Binary data, Detection and tracking algorithms, Hyperspectral target detection, Image analysis, Sensors, Mathematical modeling

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Proceedings Article | 18 May 2013 Paper

Target detection using the background model from the topological anomaly detection algorithm

David Messinger, Amanda Ziemann, Leidy Dorado Munoz

Proceedings Volume 8743, 87430M (2013) https://doi.org/10.1117/12.2015335

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Statistical analysis, Hyperspectral imaging, Algorithm development, Statistical modeling, Data modeling, Algorithms, Hyperspectral target detection

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Proceedings Article | 24 May 2012 Paper

Assessing the impact of background spectral graph construction techniques on the topological anomaly detection algorithm

David Messinger, William Basener, Amanda Ziemann, James Albano

Proceedings Volume 8390, 83901Z (2012) https://doi.org/10.1117/12.918889

KEYWORDS: Data modeling, Detection and tracking algorithms, Hyperspectral imaging, Statistical modeling, Mathematical modeling, Image sensors, Roads, RGB color model, Algorithms, Optical spheres

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SPIE Journal Paper | 29 March 2012

Metrics of spectral image complexity with application to large area search

David Messinger, Amanda Ziemann, Ariel Schlamm, William Basener

OE, Vol. 51, Issue 03, 036201, (March 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.3.036201

KEYWORDS: Hyperspectral imaging, Image processing, Image quality, Image analysis, Target detection, RGB color model, Roads, Detection and tracking algorithms, Reflectivity, Optical engineering

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