While offering powerful capabilities, the high dimensionality of hyperspectral images can make information extraction a challenge. For that reason, dimension reduction is a common data processing step. For the purpose of subpixel target detection, band selection is a dimension reduction method that can optimize results as well as reduce computation costs. However, existing band selection methods that are used for subpixel target detection require background spectral reflectance signatures to compare with the target signatures. These methods work well and offer a distinct advantage over other dimension reduction methods such as principal component analysis or nonnegative matrix factorization, but only when the background information is available. In this study, we developed a method that selected bands using only the target spectral reflectance signature. We tested this method using a utility prediction model, validated the results with real images, then cross-validated the results with simulated images that were associated with perfect truth data. We studied the detection statistics for a range of bands selected using this method and compared it to the results obtained from three other band selection methods. The motivation for developing this method was to be able to reduce the number of bands prior to collection when background information was not available. For an adaptive spectral imaging system with a tunable sensor, we would be able to optimize detection for a specific target and save data handling costs associated with transmitting, storing, and disseminating the data for information extraction. This method was also simple enough to be computed using a small on-board CPU, and modify the bands' selection criteria as the target changed.