Mr. Neil A. Soliman Profile

Neil A. Soliman

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Proceedings Article | 16 April 2008 Paper

Spectral gating in hyperspectral-augmented target tracking

Neil Soliman, Michael Mendenhall, Juan Vasquez

Proceedings Volume 6969, 696907 (2008) https://doi.org/10.1117/12.777590

KEYWORDS: Distance measurement, Hyperspectral imaging, Kinematics, Mahalanobis distance, Sensors, Data processing, Hyperspectral simulation, Video, Data modeling, Scanners

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Hyperspectral images provide scientists and engineers with the capability of precise material identification in remote sensing applications. One can leverage this data for precise track identification (ID) and incorporate the high-confidence ID in the tracking process. Our previous work demonstrates that hyperspectral-aided tracking outperforms kinematic-only tracking where multiple ambiguous situations exist. We develop a novel gating concept for hyperspectral measurements, similar in concept to the gating of the Mahalanobis distance computed from the Kalman residuals. Our spectral gating definition is based on the distance between the spectral distribution of the class ID of a track and the spectral distribution of the class ID resulting from the classification of a measurement. We further incorporate the distance between each class distribution (in spectral space) in the track association portion of our hyperspectral-aided tracker. Since functional forms of the joint probability distribution function do not exist, similarity measures such as the Kullback-Leibler divergence or Bhattacharyya distance cannot be used. Instead, we compute all pair-wise distances between all samples of the two classes and then summarize these distances in a meaningful way. This article presents our novel spectral gating approach and its use in track association. It further explores different similarity measures and their effect on spectral gating and track association.

Proceedings Article | 25 September 2007 Paper

Feature-aided tracking with hyperspectral imagery

Joshua Blackburn, Michael Mendenhall, Andrew Rice, Paul Shelnutt, Neil Soliman, Juan Vasquez

Proceedings Volume 6699, 66990S (2007) https://doi.org/10.1117/12.734937

KEYWORDS: Kinematics, Sensors, Video, Hyperspectral imaging, Detection and tracking algorithms, Prototyping, Mirrors, Scanners, Hyperspectral simulation, Visualization

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Target tracking in an urban environment presents a wealth of ambiguous tracking scenarios that cause a kinematic-only tracker to fail. Partial or full occlusions in areas of tall buildings are particularly problematic as there is often no way to correctly identify the target with only kinematic information. Feature aided tracking attempts to resolve problems with a kinematic-only tracker by extracting features from the data. In the case of panchromatic video, the features are often histograms, the same is true for color video data. In the case where tracks are uniquely different colors, more typical feature aided trackers may perform well. However, a typical urban setting has similar size, shape, and color tracks, and more typical feature aided trackers have no hopes in resolving many of the ambiguities we face. We present a novel feature aided tracking algorithm combining two-sensor modes: panchromatic video data and hyperspectral imagery. The hyperspectral data is used to provide a unique fingerprint for each target of interest where that fingerprint is the set of features used in our feature aided tracker. Results indicate an impressive 19% gain in correct track ID with our hyperspectral feature aided tracker compared to the baseline performance with a kinematic-only tracker.

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