Accurate　and　efficient　extraction　of　cultivated　land　data　is　of　great　significance　for　agricultural　resource　monitoring　and　national　food　security.　Deep-learning-based　classification　of　remote-sensing　images　overcomes　the　two　difficulties　of　traditional　learning　methods　(e.g.,　support　vector　machine　(SVM),　K-nearest　neighbors　(KNN),　and　random　forest　(RF))　when　extracting　the　cultivated　land:　(1)　the　limited　performance　when　extracting　the　same　land-cover　type　with　the　high　intra-class　spectral　variation,　such　as　cultivated　land　with　both　vegetation　and　non-vegetation　cover,　and　(2)　the　limited　generalization　ability　for　handling　a　large　dataset　to　apply　the　model　to　different　locations.　However,　the　＂pooling＂　process　in　most　deep　convolutional　networks,　which　attempts　to　enlarge　the　sensing　field　of　the　kernel　by　involving　the　upscale　process,　leads　to　significant　detail　loss　in　the　output,　including　the　edges,　gradients,　and　image　texture　details.　To　solve　this　problem,　in　this　study　we　proposed　a　new　end-to-end　extraction　algorithm,　a　high-resolution　U-Net　(HRU-Net),　to　preserve　the　image　details　by　improving　the　skip　connection　structure　and　the　loss　function　of　the　original　U-Net.　The　proposed　HRU-Net　was　tested　in　Xinjiang　Province,　China　to　extract　the　cultivated　land　from　Landsat　Thematic　Mapper　(TM)　images.　The　result　showed　that　the　HRU-Net　achieved　better　performance　(Acc:　92.81%;　kappa:　0.81;　F1-score:　0.90)　than　the　U-Net++　(Acc:　91.74%;　kappa:　0.79;　F1-score:　0.89),　the　original　U-Net　(Acc:　89.83%;　kappa:　0.74;　F1-score:　0.86),　and　the　Random　Forest　model　(Acc:　76.13%;　kappa:　0.48;　F1-score:　0.69).　The　robustness　of　the　proposed　model　for　the　intra-class　spectral　variation　and　the　accuracy　of　the　edge　details　were　also　compared,　and　this　showed　that　the　HRU-Net　obtained　more　accurate　edge　details　and　had　less　influence　from　the　intra-class　spectral　variation.　The　model　proposed　in　this　study　can　be　further　applied　to　other　land　cover　types　that　have　more　spectral　diversity　and　require　more　details　of　extraction.