Spectral DAISY: a combined target spatial-spectral dense feature descriptor for improved tracking performance

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.