Mr. Jerrold E. Baum Profile

Jerrold E. Baum

Associate Staff Member at MIT Lincoln Lab

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Publications (7)

Proceedings Article | 7 January 2004 Paper

Separability of VNIR/SWIR reflectance signatures of prepared soil samples: airborne hyperspectral vs. field measurements

Jerrold Baum, Seth Orloff, Su May Hsu, Hsiao-hua Burke

Proceedings Volume 5159, (2004) https://doi.org/10.1117/12.504598

KEYWORDS: Reflectivity, Soil science, Spectrometers, Absorption, Neural networks, Sensors, Hyperspectral imaging, Signal to noise ratio, Short wave infrared radiation, Image sensors

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Proceedings Article | 14 January 2002 Paper

Physical phenomena, hyperspectral bands, and metrics for coastal bathymetry

Jerrold Baum, Nirmal Keshava, Cheryl Jaffe

Proceedings Volume 4488, (2002) https://doi.org/10.1117/12.452817

KEYWORDS: Reflectivity, Sensors, Distance measurement, Water, Hyperspectral imaging, Physical phenomena, Data processing, Ocean optics, Signal attenuation, Solids

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Proceedings Article | 20 August 2001 Paper

Modeling of LWIR hyperspectral system performance for surface object and effluent detection applications

John Kerekes, Michael Griffin, Jerrold Baum, Kristine Farrar

Proceedings Volume 4381, (2001) https://doi.org/10.1117/12.437025

KEYWORDS: Sensors, Long wavelength infrared, Atmospheric modeling, Data modeling, Performance modeling, Signal to noise ratio, Target detection, Systems modeling, Radiative transfer, Spectroscopy

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Proceedings Article | 27 October 1999 Paper

Analysis of HYDICE noise characteristics and their impact on subpixel object detection

Melissa Nischan, John Kerekes, Jerrold Baum, Robert Basedow

Proceedings Volume 3753, (1999) https://doi.org/10.1117/12.366274

KEYWORDS: Signal to noise ratio, Sensors, Calibration, Data modeling, Reflectivity, Performance modeling, Systems modeling, Lamps, Optical coatings, Hyperspectral imaging

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Proceedings Article | 12 July 1999 Paper

Analytical model of hyperspectral system performance

John Kerekes, Jerrold Baum, Kristine Farrar

Proceedings Volume 3701, (1999) https://doi.org/10.1117/12.352969

KEYWORDS: Sensors, Reflectivity, Data modeling, Atmospheric modeling, Systems modeling, Statistical modeling, Performance modeling, Atmospheric sensing, Electro optical modeling, Atmospheric propagation

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Proceedings Article | 3 October 1998 Paper

Radiometric error in GOES 8 Imager data due to sensor MTF

Edward Wack, Jerrold Baum

Proceedings Volume 3439, (1998) https://doi.org/10.1117/12.325623

KEYWORDS: Modulation transfer functions, Sensors, Spatial frequencies, Imaging systems, Data modeling, Clouds, Point spread functions, Calibration, Atmospheric modeling, Diffraction

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Proceedings Article | 1 August 1991 Paper

Simultaneous active/passive IR vehicle detection

Jerrold Baum, Steven Rak

Proceedings Volume 1416, (1991) https://doi.org/10.1117/12.43727

KEYWORDS: Target detection, Detection and tracking algorithms, Image fusion, LIDAR, Image processing, Image filtering, Palladium, Automatic target recognition, Sensors, Infrared imaging

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The use of a range/passive-IR histogram as an approach to pixel-level fusion for cued target detection is discussed. Target detection algorithms for laser radar range imagery often use a number of computationally-intensive operations to locate targets in an image. These steps may include performing global geometric transforms, locating the ground plane, or applying size filters with associated rules. Each pixel in the image is processed multiple times, a time- consuming chore. An alternative to examining every pixel is to cue such detailed algorithms directly to an image location which is likely to contain a target. Cueing reduces the burden of searching or processing the entire image for regions of interest, greatly decreasing the number of computations needed for target detection. Cueing can be accomplished by combining registered laser radar range and passive-IR data. Since these data are taken simultaneously and are co-registered by Lincoln Laboratory's airborne laser radar, it is possible to combine them to form a powerful set of discriminants. There are two possible approaches to fusing the range and passive-IR data: (1) the domains can be processed for detections in two parallel streams and the resulting detection maps combined, or (2) the domains can be fused first and then processed as a single stream for detections. In the first method, sometimes called 'image-level' fusion, the processing algorithm for each domain can be optimized to obtain the best combination of detection and false alarm statistics. In the second method, the domains are combined at the pixel level, and the result is processed directly for target detection. The latter approach also reduces the number of computations since only data of interest to both domains is processed further. In this article, the multi-dimensional laser radar sensor and ongoing efforts to develop an automatic target recognition (ATR) system are described. The processing of pixel-registered laser radar range and passive-IR imagery for cued target detection using the range/passive-IR histogram is discussed. The authors present results for IR imagery with positive passive-IR target-to-background contrast to study the performance of the algorithm in real-world scenarios. These results are compared with a more typical detection method.

Showing 5 of 7 publications

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