In　this　paper,　TK　(Tomasi-Kanade)　corner-detector　and　COVPEX　(Corner　validation　based　on　corner　property　extraction,　COVPEX)　algorithm　are　combined　to　extract　corners　from　IKONOS　Multi-spectral　images.　Firstly,　we　use　TK　corner-detector　and　find　that　the　detector　is　sensitive　to　the　corner-orientation　and　corner-contrast　changes.　It　always　results　in　＇under-detection＇.　Aim　at　these　defects　of　TK　corner-detector,　we　improve　it　and　propose　the　Multi-spectral　double-directional　TK　corner　detector.　To　reduce　sensitive　degree　to　the　corner-contrast,　the　new　detector　uses　multi-spectral　data　to　estimate　corner-significance　of　a　pixel.　Moreover,　the　corner-clusters　are　presented　in　the　processing　results,　which　destroy　uniqueness　of　corner.　In　order　to　reduce　the　proportion　of　pseudo-corners,　we　propose　multi-scale　COVPEX　algorithm　which　uses　multi-scale　corner-characters　to　validate　corners.　In　the　meantime,　based　on　the　local　minimum　value　theory,　the　corner-cluster　removing　method　is　also　proposed　to　preserve　uniqueness　of　corner.　Finally,　the　comparative　experiment　results　of　corner　extraction　show　that　the　proposed　method　is　suitable　for　multi-spectral　high-resolution　imagery,　accuracy　and　rationality　of　the　extracted　corners　are　considerably　improved.