A　new　algorithm　of　unmanned　aerial　vehicle　landforms　image　classification　based　on　sparse　autoencoder(SAE)　is　proposed　in　view　of　the　drawbacks　of　single　layer　sparse　autoencoder　for　feature　learning　that　it　is　easy　to　lose　the　deep　abstract　feature　and　the　feature　lacks　the　robustness.　In　this　paper,　first,　by　constructing　the　deep　sparse　autoencoder,　the　image　layer　by　layer　learning　and　automatically　extract　each　layer　features.　Then,　in　order　to　improve　the　feature　representations,　the　each　layer　feature　weights　and　the　reorganized　feature　set　are　obtained　according　to　the　feature　set　weighting　method.　Finally,　combining　the　strong　global　search　ability　of　genetic　algorithm　(GA)　and　the　excellent　performance　of　support　vector　machine　(SVM),　the　image　classification　is　completed　efficiently　and　accurately.　The　experimental　results　show　that　the　proposed　algorithm　can　automatically　learn　the　deep　feature　of　the　image,　and　the　reorganized　feature　has　high　discriminations　image　representations,　which　effectively　improves　the　image　classification　accuracy.　©　2017　IEEE.