Diffusion features for target specific recognition with synthetic aperture sonar raw signals and acoustic color

Given a high dimensional dataset, one would like to be able to represent this data using fewer parameters while preserving relevant signal information. If we assume the original data actually exists on a lower dimensional manifold embedded in a high dimensional feature space, then recently popularized approaches based in graph-theory and differential geometry allow us to learn the underlying manifold that generates the data. One such technique, called Diffusion Maps, is said to preserve the local proximity between data points by first constructing a representation for the underlying manifold. This work examines target specific classification problems using Diffusion Maps to embed inverse imaged synthetic aperture sonar signal data for automatic target recognition. The data set contains six target types. Results demonstrate that the diffusion features capture suitable discriminating information from the raw signals and acoustic color to improve target specific recognition with a lower false alarm rate. However, fusion performance is degraded.