In　view　of　the　fact　that　the　SIFT　algorithm　extracts　the　feature　points　too　little　and　ignores　their　distribution,　and　also　has　the　problem　of　high　computation　cost,　a　discrete-scale-invariant　feature　extraction　algorithm　(discrete　SIFT　or　DSIFT　in　short)　is　proposed.　This　algorithm　introduces　a　sliding　window　on　the　space　extreme　test　phase　of　the　SIFT　algorithm,　implements　non-maximum　suppression　for　the　extreme　points　detection　inside　the　window,　so　that　the　feature　points　are　distributed　relatively　even.　In　addition,　this　algorithm　makes　the　calculation　faster,　and　at　the　same　time　maintains　the　scale,　rotation,　and　affinity　invariant.　In　order　to　reduce　the　time　overhead　of　the　image　registration　in　various　stages,　it　adds　a　desampling　operation　before　feature　extraction　and　the　operation　of　location　information　reversion　before　calculating　the　homographic　matrix.　And　it　also　introduces　K-Dimensional　Tree　in　the　process　of　searching　matching　points,　and　adopts　RANSAC　algorithm　in　the　shifting　of　the　feature　points　and　estimation　of　homographic　matrix.　Finally,　through　experiment　verification,　it　is　found　that　the　DSIFT　algorithm　has　more　uniform　distribution　of　feature　points　than　SIFT　algorithm,　and　with　high　robustness.　At　the　same　time,　on　the　premise　of　guaranteeing　the　quality　of　image　mosaicking,　the　time　overhead　in　various　stages　of　image　registration　is　greatly　reduced.　©,　2015,　Xi＇an　Jiaotong　University.　All　right　reserved.