  Anomalous change detection (ACD) methods separate common, uninteresting
changes from rare, significant changes in co-registered images collected at
different points in time. In this paper we evaluate methods to improve the
performance of ACD in detecting human activity in SAR imagery using outdoor
music festivals as a target. Our results show that the low dimensionality of
SAR data leads to poor performance of ACD when compared to simpler methods such
as image differencing, but augmenting the dimensionality of our input feature
space by incorporating local spatial information leads to enhanced performance.
