We describe a new approach for performing pseudo-imaging of point energy sources from spectral-temporal sensor data collected using a rotating-prism spectrometer. Pseudo-imaging, which involves the automatic localization, spectrum estimation, and identification of energetic sources, can be difficult for dim sources and/or noisy images, or in data containing multiple sources which are closely spaced such that their signatures overlap, or where sources move during data collection. The new approach is specifically designed for these difficult cases. It is developed within an iterative, maximum-entropy, framework which incorporates an efficient optimization over the space of all model parameters and mappings between image pixels and sources, or clutter. The optimized set of parameters is then used for detection, localization, tracking, and identification of the multiple sources in the data. The paper includes results computed from experimental data.