Dimensionality　reduction　techniques　have　become　an　important　issue　concerns　of　hyper-spectral　image　processing　and　application　A　semi-supervised　dimensionality　reduction　(SSDR)　for　classification　of　hyper-spectral　image　is　applied　in　this　paper.　This　method　employed　both　labeled　and　unlabeled　data　with　pairwise-constraints　to　obtain　a　set　of　projective　vectors　such　that　intrinsic　structures　of　image　as　well　as　the　pairwise　constraints　can　be　preserved　in　the　projective　low-dimensional　space.　To　evaluate　the　method,　a　case　study　of　Airborne　VisiblelInfrared　Imaging　Spectrometer　(AVIRIS)　image　is　implemented,　and　the　experimental　results　validate　the　applicability　and　effective　of　the　algorithm.　Comparisons　with　principal　component　analysis　(PCA)　and　Fisher　discriminate　analysis　(FDA)　are　also　conducted,　and　the　result　demonstrates　that　the　SSDR　can　significantly　improve　classification　accuracy.　©　2010　IEEE.