On the Impact of Covariance Contamination for Adaptive Detection in Hyperspectral Imaging

Adaptive detection has been a staple in areas such as radar and sonar for a number of years. A key tenet of adaptive detectors is their use of a “noise only” covariance estimate from a secondary data set. When samples bearing the target signal are included in this covariance estimate, it becomes contaminated. The impact of covariance contamination is evaluated for three popular adaptive detection statistics. Multiple test cases are presented using real data, not simulations, and it is shown that significant contamination occurs with as few as eight target samples included in a 100 000+ sample covariance estimate. In addition to number of samples, sensitivity of the detection statistic to contamination from targets present also depends on algorithm type. Hyperspectral imaging (HSI) data offer insight into the impact of covariance contamination on adaptive detection and how it is related to signal-to-noise ratio (SNR) and spectral similarity.