We discuss an algorithmic approach for detecting spatially stationary, dim signals in cluttered optical data. In the problem considered here, cluttered scene backgrounds are substantially more intense than sensor noise and signal variations from scene anomalies of interest. As a result, clutter estimation and rejection algorithms are performed prior to implementing signal detection schemes. Even then, stationary residual clutter may be spatially similar to, and have intensities much greater than, those of the signals of interest. This poses an extreme challenge for the automated detection of low-contrast scene anomalies, and detectors based solely on spatial properties of the optical scene generally fail. In our newly developed signal detection algorithm, we exploit not only the structure of the dim signals of interest, but also the time-lapsed residual clutter. By examining the properties and statistics of both the signals of interest and the signals we wish to reject, Toyon has developed an algorithm for the automated detection of low-contrast signals in the presence of high-intensity clutter. We discuss here the developed signal detection algorithm and results for overcoming the challenges inherent to heavily cluttered optical data.