Hyperspectral band selection extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that using a subset of original bands the accuracy is unchanged or tolerably degraded while computational burden is significantly relaxed. When the desired object information is known, i.e., supervised band selection, this task can be achieved by finding the bands that contain the most information about these objects. When the desired object information is unknown, i.e., unsupervised band selection, the objective is to select the most distinctive and informative bands. It is expected that these bands can provide an overall satisfactory detection and classification performance. We propose an unsupervised band selection algorithm based on band similarity measurement, which can yield a better result in terms of information conservation and class separability than other widely used techniques. We also extend this algorithm to the case when the desired object information is known. The experimental result shows the effectiveness of this new algorithm.