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Proceedings Paper

Spectral DAISY: a combined target spatial-spectral dense feature descriptor for improved tracking performance
Author(s): Jeffrey J. Weinheimer; Pierre Villeneuve; Scott G. Beaven
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Paper Abstract

In EO tracking, target spatial and spectral features can be used to improve performance since they help distinguish the targets from each other when confusion occurs during normal kinematic tracking. In this paper we introduce a method to encode a target's descriptive spatial information into a multi-dimensional signature vector, allowing us to convert the problem of spatial template matching into a form similar to spectral signature matching. This allows us to leverage multivariate algorithms commonly used with hyperspectral data to the problem of exploiting panchromatic imagery. We show how this spatial signature formulation naturally leads to a hybrid spatial-spectral descriptor vector that supports exploitation using commonly-used spectral algorithms. We introduce a new descriptor called Spectral DAISY for encoding spatial information into a signature vector, based on the concept of the DAISY dense descriptor. We demonstrate the process on real data and show how the combined spatial/spectral feature can be used to improve target/track association over spectral or spatial features alone.

Paper Details

Date Published: 17 September 2011
PDF: 15 pages
Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 813706 (17 September 2011); doi: 10.1117/12.892816
Show Author Affiliations
Jeffrey J. Weinheimer, Space Computer Corp. (United States)
Pierre Villeneuve, Space Computer Corp. (United States)
Scott G. Beaven, Space Computer Corp. (United States)


Published in SPIE Proceedings Vol. 8137:
Signal and Data Processing of Small Targets 2011
Oliver E. Drummond, Editor(s)

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