Improving Background Multivariate Normality and Target Detection Performance Using Spatial and Spectral Segmentation

摘要

Target detection in reflective hyperspectral imagery generally involves the application of a spectral matched filter on a per-pixei basis to create an image of the target likelihood of occupying each pixel. Stochastic (or unstructured) target detection techniques require the user to define an estimate of the background mean and covariance from which to separate out the desired targets in the image. Typically, scene-wide statistics are used, although it Is simple to show that this methodology does not produce sufficiently multivariate normal bachgrounds nor does it necessarily represent the best suppression of likely false alarms.