A　kernel　orthogonal　subspace　projection　(KOSP)　algorithm　has　been　developed　for　nonlinear　approximating　subpixel　proportion　in　this　paper.　The　algorithm　applies　linear　regressive　model　to　the　feature　space　induced　by　a　Mercer　kernel,　and　can　therefore　be　used　to　recursively　construct　the　minimum　mean　squared-error　regressor.　The　algorithm　includes　two　steps:　the　first　step　is　to　select　the　feature　vectors　by　defining　a　global　criterion　to　characterize　the　image　data　structure　in　the　feature　space;　and　the　second　step　is　the　projection　onto　the　feature　vectors　and　then　apply　the　classical　linear　regressive　algorithm.　Experiments　using　synthetic　data　degraded　by　an　AVIRIS　image　have　been　carried　out,　and　the　results　demonstrate　that　the　proposed　method　can　provide　excellent　proportion　estimation　for　hyperspectral　images.　Comparison　with　support　vector　regression　(SVR)　and　radial　basis　function　neutral　network　(RBF)　had　also　been　given,　and　the　experiments　show　that　the　proposed　algorithm　slightly　outperform　than　RBF　and　SVR.　©　Springer-Verlag　Berlin　Heidelberg　2006.