Multi-pass search and reconnaissance missions provide unique opportunities for hyperspectral target detection systems to operate at drastically reduced false alarm rates. In the simplest example, confirmed false alarms generated by an anomaly detector on an initial pass can be archived for future reference. A more sophisticated approach captures false alarm signatures to update background clutter models that inform detection algorithms. Or if detections are confirmed as targets on one pass, then matched-filter maps based on their spectra can be compared over time to monitor changes. As a final example, when sub-pixel registration is feasible, multi-temporal spectral covariance relations can be estimated from the data and used to detect anomalous changes at low false alarm rates, using no target signature information. All but the simplest of such methods require that the spectral evolution of a terrestrial background -- its chromodynamics -- be modeled sufficiently that naturally occurring changes are not confused with unnatural ones. This paper describes several detection paradigms that rely on multi-pass missions. Optimal linear algorithms to predict scene and target evolution are discussed, as are more realistic methods with relaxed operational requirements. One of these, called Covariance Equalization, is shown to perform nearly as well as the minimum error solution based on the matrix Wiener filter, which requires subpixel registration accuracy.