Remote　sensing　research　focusing　on　feature　selection　has　long　attracted　the　attention　of　the　remote　sensing　community　because　feature　selection　is　a　prerequisite　for　image　processing　and　various　applications.　Different　feature　selection　methods　have　been　proposed　to　improve　the　classification　accuracy.　They　vary　from　basic　search　techniques　to　clonal　selections,　and　various　optimal　criteria　have　been　investigated.　Recently,　methods　using　dependence-based　measures　have　attracted　much　attention　due　to　their　ability　to　deal　with　very　high　dimensional　datasets.　However,　these　methods　are　based　on　Cramer＇s　V　test,　which　has　performance　issues　with　large　datasets.　In　this　paper,　we　propose　a　parallel　approach　to　improve　their　performance.　We　evaluate　our　approach　on　hyper-spectral　and　high　spatial　resolution　images　and　compare　it　to　the　proposed　methods　with　a　centralized　version　as　preliminary　results.　The　results　are　very　promising.