A　landform　image　classification　algorithm　based　on　sparse　coding　and　convolutional　neural　network　is　proposed.　The　non-subsampled　Contourlet　transform　is　applied　to　the　training　samples　for　multi-scale　decomposition.　The　images　are　selected　in　the　training　samples　to　learn　the　local　features　by　using　sparse　coding,　and　the　feature　vectors　are　sorted.　The　feature　vectors　with　larger　gray-scale　mean　gradients　are　selected　to　initialize　the　convolutional　neural　network　convolution　kernel.　The　results　show　that　the　proposed　algorithm　can　obtain　better　classification　results　than　traditional　underlying　visual　features,　which　effectively　avoids　the　problem　of　network　training　falling　into　local　optimum,　and　improves　the　classification　accuracy　of　unmanned　aerial　vehicles　landing　landform　in　natural　scenes.　©　2019,　Chinese　Lasers　Press.　All　right　reserved.