In　remote　sensing　data　processing,　band　selection　is　very　important　for　hyperspectral　image　processing　and　analysis,　which　utilize　the　most　distinctive　and　informative　band　subset　of　original　bands　to　reduce　data　dimensionality.　Although　band　selection　can　significantly　alleviate　the　computational　burden,　the　process　itself　may　cause　additional　computation　complexity.　In　this　paper,　an　unsupervised　band　selection　method　based　on　band　similarity　is　proposed　for　hyperspectral　image　target　detection.　Several　selected　pixels　are　used　for　unsupervised　band　selection　instead　of　using　all　the　pixels　to　reduce　computational　complexity.　The　number　of　bands　to　be　selected　is　determined　by　adjusting　the　threshold　of　similarity　metric,　to　ensure　target　detection　operator　have　the　best　performance　with　selected　bands.　The　experimental　results　show　that　our　method　can　yield　a　better　result　in　target　detection.　Copyright　2014　ACM.