Mr. Benny Aminov Profile

Benny Aminov

at Ben-Gurion Univ of the Negev

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Proceedings Article | 2 October 2008 Paper

Spatial and temporal point tracking in real hyperspectral images

Benjamin Aminov, Ofir Nichtern, S. Rotman

Proceedings Volume 7113, 711316 (2008) https://doi.org/10.1117/12.799634

KEYWORDS: Detection and tracking algorithms, Target detection, Signal to noise ratio, Hyperspectral target detection, Hyperspectral imaging, Infrared imaging, Infrared technology, Computer programming, Image filtering, Transform theory

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This paper addresses the problem of tracking a dim moving point target from a sequence of hyperspectral cubes. The resulting tracking algorithm is useful for many staring technologies such as the ones used in space surveillance and missile tracking applications. In these applications, the images consist of targets moving at sub-pixel velocity and noisy background consisting of evolving clutter and noise. The demand for a low false alarm rate (FAR) on one hand and a high probability of detection (PD) on the other makes the tracking a challenging task. The use of hyperspectral images should be superior to current technologies using broadband IR images due to the ability of exploiting simultaneously two target specific properties: the spectral target characteristics and the time dependent target behavior. The proposed solution consists of three stages: the first stage transforms the hyperspectral cubes into a two dimensional sequence, using known point target detection acquisition methods; the second stage involves a temporal separation of the 2D sequence into sub-sequences and the usage of a variance filter (VF) to detect the presence of targets from the temporal profile of each pixel in each group, while suppressing clutter specific influences. This stage creates a new sequence containing a target with a seemingly faster velocity; the third stage applies the Dynamic Programming Algorithm (DPA) that proves to be a very effective algorithm for the tracking of moving targets with low SNR at around pixel velocity. The system is tested on both synthetic and real data.

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