The　accumulation　of　serial　remote　sensing　images　provides　plentiful　data　for　discovering　sequential　spatial　patterns　in　various　fields　such　as　agricultural　monitoring,　urban　development,　and　vegetation　cover.　Otherwise,　traditional　sequential　pattern-mining　algorithms　cannot　be　directly　or　efficiently　applied　to　remote　sensing　images.　In　this　study,　we　propose　a　pixel　clustering-based　method　to　improve　the　efficiency　of　mining　spatial　sequential　patterns　from　raster　serial　remote　sensing　images　(SRSI).　Firstly,　the　images　are　compressed　by　using　the　Run-Length　coding　schema.　Then,　pixels　with　identical　sequences　are　clustered　by　means　of　the　Run-length　code-based　spatial　overlay　operation.　Finally,　a　pruning　strategy　is　proposed,　to　extend　the　prefixSpan　algorithm　to　skip　unnecessary　database　scanning　when　mining　from　pixel　groups.　The　experimental　results　indicate　that　the　method　presented　in　this　paper　could　extract　spatial　sequential　patterns　from　SRSI　efficiently.　Although　accurate　support　rates　for　the　patterns　may　not　be　obtained,　our　method　could　ensure　that　all　patterns　are　extracted　with　a　lower　time　cost.　©　2019　Elsevier　Ltd