A　feature　selection　method　based　on　Cramer＇s　V-test　(CV-test)　discretization　is　presented　to　improve　the　classification　accuracy　of　remotely　sensed　imagery.　Three　possible　contributions　are　pursued　in　this　paper.　First　of　all,　a　Cramer＇s　V-based　discretization　(CVD)　algorithm　is　proposed　to　optimally　partition　the　continuous　features　into　discrete　ones.　Two　association-based　feature　selection　indexes,　the　CVD-based　association　index　(CVDAI)　and　the　class-attribution　interdependence　maximization　(CAIM)-based　association　index　(CAIMAI),　derived　from　the　CV-test　value,　are　then　proposed　to　select　the　optimal　feature　subset.　Finally,　the　benefit　of　using　discretized　features　to　improve　the　performance　with　the　J48　decision　tree　(J48-DT)　and　naive　Bayes　(NB)　classifiers　is　studied.　To　validate　the　proposed　approaches,　a　high　spatial　resolution　image　and　two　hyperspectral　data　sets　were　used　to　evaluate　the　performances　of　CVD　and　the　associated　algorithms.　The　test　performances　of　discretization　using　CVD　and　two　other　state-of　-the-art　methods,　the　CAIM　and　equal　width,　show　that　the　CVD-based　technique　has　the　better　ability　to　generate　a　good　discretization　scheme.　Furthermore,　the　feature　selection　indexes,　CVDAI　and　CAIMAI,　perform　better　than　the　other　used　feature　selection　methods　in　terms　of　overall　accuracies　achieved　by　the　J48-DT,　NB,　and　support　vector　machine　classifiers.　Our　tests　also　show　that　the　use　of　discretized　features　benefits　the　J48-DT　and　NB　classifiers.