While　SIFT　(Scale　Invariant　Feature　Transform)　features　are　used　to　match　High-Resolution　(HR)　remote　sensing　urban　images　captured　at　different　phases　with　large　scale　and　view　variations,　feature　points　are　few　and　the　matching　accuracy　is　low.　Although　replacing　SIFT　with　fully　affine　invariant　features　ASIFT　(Affine-SIFT)　can　increase　the　number　of　feature　points,　it　results　in　matching　inefficiency　and　a　non-uniform　distribution　of　matched　feature　point　pairs.　To　address　these　problems,　this　paper　proposes　the　novel　matching　method　ICA-ASIFT,　which　matches　HR　remote　sensing　urban　images　captured　at　different　phases　by　using　an　Independent　Component　Analysis　algorithm　(ICA)　and　ASIFT　features　jointly.　First,　all　possible　affine　deformations　are　modeled　for　the　image　transform,　extracting　ASIFT　features　of　remote　sensing　images　captured　at　different　times.　The　ICA　algorithm　reduces　the　dimensionality　of　ASIFT　features　and　improves　matching　efficiency　of　subsequent　ASIFT　feature　point　pairs.　Next,　coarse　matching　is　performed　on　ASIFT　feature　point　pairs　through　the　algorithms　of　Nearest　Vector　Angle　Ratio　(NVAR),　Direction　Difference　Analysis　(DDA)　and　RANdom　SAmple　Consensus　(RANSAC),　eliminating　apparent　mismatches.　Then,　fine　matching　is　performed　on　rough　matched　point　pairs　using　a　Neighborhoodbased　Feature　Graph　Matching　algorithm　(NFGM)　to　obtain　final　ASIFT　matching　point　pairs　of　remote　sensing　images.　Finally,　final　matching　point　pairs　are　used　to　compute　the　affine　transform　matrix.　Matching　HR　remote　sensing　images　captured　at　different　phases　is　achieved　through　affine　transform.　Experiments　are　used　to　compare　the　performance　of　ICA-ASFIT　and　three　other　algorithms　(i.e.,　Harris-　SIFT,　PCA-SIFT,　TD-ASIFT)　on　HR　remote　sensing　images　captured　at　different　times　in　different　regions.　Experimental　results　show　that　the　proposed　ICA-ASFIT　algorithm　effectively　matches　HR　remote　sensing　urban　images　and　outperforms　other　algorithms　in　terms　of　matching　accuracy　and　efficiency.