  In this paper, two methods for multiple instance target characterization,
MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative
target signature from imprecisely-labeled and mixed training data. In many
applications, such as sub-pixel target detection in remotely-sensed
hyperspectral imagery, accurate pixel-level labels on training data is often
unavailable and infeasible to obtain. Furthermore, since sub-pixel targets are
smaller in size than the resolution of a single pixel, training data is
comprised only of mixed data points (in which target training points are
mixtures of responses from both target and non-target classes). Results show
improved, consistent performance over existing multiple instance concept
learning methods on several hyperspectral sub-pixel target detection problems.
