Abstract
Abstract In this article, we consider the problem of tracking a point target moving against a background of sky and clouds. The proposed solution consists of three stages: the first stage transforms the hyperspectral cubes into a two-dimensional (2D) temporal sequence using known point target detection acquisition methods; the second stage involves the temporal separation of the 2D sequence into sub-sequences and the usage of a variance filter (VF) to detect the presence of targets using the temporal profile of each pixel in its 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 tracks moving targets with low SNR at around pixel velocity. The system is tested on both synthetic and real data.
Highlights
In the intervening years, interest in hyperspectral sensing has increased dramatically, as evidenced by advances in sensing technology and planning for future hyperspectral missions, increased availability of hyperspectral data from airborne and space-based platforms, and development of methods for analyzing data and new applications [1].This article addresses the problem of tracking a dim moving point target from a sequence of hyperspectral cubes
We posit that the use of hyperspectral images will be superior to current technologies using broadband IR images due to the ability of the hyperspectral image technique to simultaneously exploit two target-specific properties: the spectral target characteristics and the time-dependent target behavior
We present an overall integration of the system; in particular, the article analyzes the integration of the variance filter (VF) and the Dynamic Programming Algorithm (DPA) and provides an overall evaluation of the system
Summary
Interest in hyperspectral sensing has increased dramatically, as evidenced by advances in sensing technology and planning for future hyperspectral missions, increased availability of hyperspectral data from airborne and space-based platforms, and development of methods for analyzing data and new applications [1]. The optimal variance window for the overall algorithm’s performance is the one offering the best compromise between the need to enhance the target profile score (i.e., as short as possible) and the need to suppress the shortterm noise fluctuations (i.e., as long as possible). The metric defined for assessing the overall performance of the algorithm, which is given, takes into consideration the target enhancement and the ability of the algorithm to suppress the background This is achieved by grading each block with a score that evaluates the difference between the maximal 5 values of the block and the block average values, normalized by the standard
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