Vehicle　classification　plays　an　important　part　in　Intelligent　Transport　System　(ITS).　However,　the　existing　vehicle　classification　methods　are　not　very　robust　to　various　changes　such　as　lighting,　weathers,　noises,　and　the　classification　accuracy　has　been　requiring　to　be　improved.　Sparse　Representation-based　Classifier　(SRC)　is　not　sensitive　to　the　shortage　and　damage　of　data,　the　feature　selection　methods,　is　robust　to　lighting　changes　of　the　images,　and　can　achieve　excellent　performance　in　multi-class　classification　problems.　Therefore,　this　paper　presents　a　new　method　based　on　spatial　location　information　and　SRC.　Firstly,　taken　the　specific　characteristics　of　vehicles　into　consideration,　the　features　of　HOG　(Histogram　of　Oriented　Gradient)　and　HU　moments　are　extracted　to　characterize　the　property　of　vehicles.　In　addition,　the　spatial　location　information　is　added　to　HU　moment　features　in　this　paper　to　improve　its　ability　to　distinguish　and　describe　vehicles.　Then,　vehicles　are　classified　into　six　classes　(large　bus,　car,　motorcycle,　minibus,　truck　and　van)　by　SRC.　The　performance　evaluation　is　carried　out　on　the　dataset　which　consists　of　13300　vehicle　images　extracted　from　the　real　highway　surveillance　videos.　The　experimental　results　show　that,　using　the　proposed　method,　the　time　cost　of　classification　can　be　speeded　up　by　about　1.57　times　and　the　average　classification　precision　can　achieve　96.53%,　which　is　drastically　improved　by　more　than　2.7%　compared　to　other　existing　methods.　©　2016　IEEE.