Remote passive sensors can collect data that depict both the spatial distribution of objects in the scene and the spectral distributions for those objects within the scene. Target search techniques, such as matched filter algorithms, use highly resolved wavelength spectra (large number of bands) to help detect fine features in the spectrum in order to discriminate objects from the background. The use of a large number of bands during the target search, however, significantly slows image collection and area coverage rates. This study quantitatively examines how binning or integrating bands can affect target detection. Our study examines the long-wave infrared spectra of man-made targets and natural backgrounds obtained with the SEBASS (8-12 micrometers ) imager as part of the Dark HORSE 2 exercise during the HYDRA data collection in November, 1998. In this collection, at least 30 bands of data were obtained, but they were then binned to as few as 2 bands. This study examines the effect on detection performance of reducing the number of bands, through computation of the signal to clutter ratio (SCR) for a variety of target types. In addition, this study examines how band reduction affects the receiver operator curves (ROC) i.e. the target detection probability versus false alarm rate, for matched filter algorithms using in-scene target signatures and hyperspectral images. Target detection, as measured by SCR, for a variety of target types, improves with increasing number of bands. The enhancement in SCR levels off at approximately 10 bands, with only a small increase in SCR obtained from 10 to 30 bands. Variable number of bands within a bin (for fixed number of bins), generated by a genetic algorithm, increases SCR and ROC curve performance for multi-temporal studies. Thus, optimal selection of bands derived from one mission, may be robust and stable, and provide enhanced target detection for data collected on subsequent days. This investigation is confined to the study of calibrated, LWIR image cubes where clutter, rather than sensor noise, limits target detection. Therefore, many of the conclusions in this study regarding band reduction and band binning may not apply to image cubes containing noisy data, where band reduction and averaging may help substantially reduce the noise.