Classification　of　Hyperspectral　Images　(HSIs)　has　gained　attention　for　the　past　few　decades.　In　remote　sensing　image　classification,　the　labeled　samples　are　insufficient　or　hard　to　obtain;　however,　the　unlabeled　ones　are　frequently　rich　and　of　a　vast　number.　When　there　are　no　sufficient　labeled　samples,　overfitting　may　occur.　To　resolve　the　overfitting　issue,　in　this　present　work,　we　proposed　a　novel　approach　for　HSI　feature　extraction,　called　robust　regularized　Block　Low-Rank　Discriminant　Analysis　(BLRDA),　which　is　a　robust　and　efficient　feature　extraction　method　to　improve　the　HSIs＇　classification　accuracy　with　few　labeled　samples.　To　reduce　the　exponentially　growing　computational　complexity　of　the　low-rank　method,　we　divide　the　entire　image　into　blocks　and　implement　the　low-rank　representation　for　each　block　respectively.　Due　to　the　symmetric　matrix　requirements　for　the　regularized　graph　of　discriminant　analysis,　the　k-nearest　neighbor　is　applied　to　handle　the　whole　low-rank　graph　integrally.　The　low-rank　representation　and　the　kNN　can　maximally　capture　and　preserve　the　global　and　local　geometry　of　the　data,　respectively,　and　the　performance　of　regularized　discriminant　analysis　feature　extraction　can　be　apparently　improved.　Extensive　experiments　on　multi-class　hyperspectral　images　show　that　the　proposed　BLRDA　is　a　very　robust　and　efficient　feature　extraction　method.　Even　with　simple　supervised　and　semi-supervised　classifiers　(nearest　neighbor　and　SVM)　and　randomly　given　parameters,　the　feature　extraction　method　achieves　significant　results　with　few　labeled　samples,　which　shows　better　performance　than　similar　feature　extraction　methods.