In　the　unknown　environment,　the　automatic　identification　and　classification　of　unmanned　aerial　vehicle　(UAV)　landing　landforms　are　of　great　significance.　The　traditional　natural　scene　classification　uses　the　information　of　the　middle-　and　the　low-level　features,　but　the　UAV　landing　landform　image　has　complex　scene　and　rich　information,　which　needs　high-level　semantic　features　to　express　more　accurate　information.　A　landform　image　classification　algorithm　based　on　discrete　cosine　transform　(DCT)　and　deep　network　is　proposed.　First,　the　advantage　of　DCT　energy　concentration　is　introduced　into　the　efficient　feature　representation　of　convolutional　neural　network　(CNN)　to　reduce　the　dimensionality　and　computational　complexity.　Then　a　14-layer　feature　learning　network　is　constructed　based　on　the　characteristics　of　landform　image,　and　the　CNN　structure　is　improved.　Finally,　the　deep　features　are　input　into　the　support　vector　machine　(SVM)　to　complete　the　image　classification　quickly　and　accurately.　Experimental　results　show　that　the　algorithm　reduces　data　redundancy　and　training　time　greatly,　and　can　automatically　learn　high-level　semantic　features.　The　features　extracted　by　the　proposed　algorithm　have　better　feature　expressions　and　effectively　improve　the　image　classification　accuracy.　©　2018,　Chinese　Lasers　Press.　All　right　reserved.