Ms. Emily E. Berkson Profile

Emily E. Berkson

Research Scientist Sr at Lockheed Martin Space Systems Co

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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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Communities surrounding local airports are becoming increasingly concerned about the aircraft pollutants emitted during the landing-takeoff (LTO) cycle, and their potential for negative health effects. Chicago, Los Angeles, Boston and London have all recently been featured in the news regarding concerns over the amount of airport pollution being emitted on a daily basis, and several studies have been published on the increased risks of cancer for those living near airports. There are currently no inexpensive, portable, and unobtrusive sensors that can monitor the spatial and temporal nature of jet engine exhaust plumes. In this work we seek to design a multispectral imaging system that is capable of tracking exhaust plumes during the engine idle phase, with a specific focus on unburned hydrocarbon (UHC) emissions. UHCs are especially potent to local air quality, and their strong absorption features allow them to act as a spatial and temporal plume tracer. Using a Gaussian plume to radiometrically model jet engine exhaust, we have begun designing an inexpensive, portable, and unobtrusive imaging system to monitor the relative amount of pollutants emitted by aircraft in the idle phase. The LWIR system will use two broadband filters to detect emitted UHCs. This paper presents the spatial and temporal radiometric models of the exhaust plume from a typical jet engine used on 737s. We also select filters for plume tracking, and propose an imaging system layout for optimal detectibility. In terms of feasibility, a multispectral imaging system will be two orders of magnitude cheaper than current unobtrusive methods (PTR-MS) used to monitor jet engine emissions. Large-scale impacts of this work will include increased capabilities to monitor local airport pollution, and the potential for better-informed decision-making regarding future developments to airports.

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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Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution; however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

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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Aircraft pollutants emitted during the landing-takeoff (LTO) cycle have significant effects on the local air quality surrounding airports. There are currently no inexpensive, portable, and unobtrusive sensors to quantify the amount of pollutants emitted from aircraft engines throughout the LTO cycle or to monitor the spatial-temporal extent of the exhaust plume. We seek to thoroughly characterize the unburned hydrocarbon (UHC) emissions from jet engine plumes and to design a portable imaging system to remotely quantify the emitted UHCs and temporally track the distribution of the plume. This paper shows results from the radiometric modeling of a jet engine exhaust plume and describes a prototype long-wave infrared imaging system capable of meeting the above requirements. The plume was modeled with vegetation and sky backgrounds, and filters were selected to maximize the detectivity of the plume. Initial calculations yield a look-up chart, which relates the minimum amount of emitted UHCs required to detect the presence of a plume to the noise-equivalent radiance of a system. Future work will aim to deploy the prototype imaging system at the Greater Rochester International Airport to assess the applicability of the system on a national scale. This project will help monitor the local pollution surrounding airports and allow better-informed decision-making regarding emission caps and pollution bylaws.

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