Kernel-Based Detection Techniques for Hyperspectral Imagery
ARMY RESEARCH LAB ADELPHI MD
Pagination or Media Count:
In this paper we implement various linear and nonlinear subspace-based anomaly detectors for hyperspectral imagery. First, a dual window technique is used to separate the local area around each pixel into two regions - an inner-window region IWR and an outer-window region OWR. Pixel spectra from each region are projected onto a subspace which is defined by projection bases that can be generated in several ways. Here we use three common pattern classification techniques Principal Component Analysis PCA, Fisher Linear Discriminant FLD Analysis, and the Eigenspace Separation Transform EST to generate projection vectors. In addition to these three algorithms, the well-known Reed-Xiaoli RX anomaly detector is also implemented. Each of the four linear methods is then implicitly defined in a high- possibly infinite- dimensional feature space by using a nonlinear mapping associated with a kernel function. Using a common machine-learning technique known as the kernel trick all dot products in the feature space are replaced with a Mercer kernel function defined in terms of the original input data space.
- Atomic and Molecular Physics and Spectroscopy