In　this　paper,　an　approach　for　robust　matching　shadow　areas　in　autonomous　visual　navigation　and　planetary　landing　is　proposed.　The　approach　begins　with　detecting　shadow　areas,　which　are　extracted　by　Maximally　Stable　Extremal　Regions　(MSER).　Then,　an　affine　normalization　algorithm　is　applied　to　normalize　the　areas.　Thirdly,　a　descriptor　called　Multiple　Angles-SIFT　(MA-SIFT)　that　coming　from　SIFT　is　proposed,　the　descriptor　can　extract　more　features　of　an　area.　Finally,　for　eliminating　the　influence　of　outliers,　a　method　of　improved　RANSAC　based　on　Skinner　Operation　Condition　is　proposed　to　extract　inliers.　At　last,　series　of　experiments　are　conducted　to　test　the　performance　of　the　approach　this　paper　proposed,　the　results　show　that　the　approach　can　maintain　the　matching　accuracy　at　a　high　level　even　the　differences　among　the　images　are　obvious　with　no　attitude　measurements　supplied.