关键词: 斑块识别, 深度学习, 耕地细碎化评价, 丘陵区, U-Net+SE模型, 高分辨率遥感, 边界提取

Abstract: To address low efficiency in cropland extraction and fragmentation evaluation in such hilly areas，this study took Shidong Town，Anju District，Suining City，Sichuan Province—a typical hilly region—as the test area，and adopted the U‑Net+SE model using Gaofen‑2 imagery for cropland patch identification and boundary extraction，respectively.Then，the fragmentation evaluation system was created and implemented from the dimension of parcel area，shape and spatial distribution.Results showed that patch identification achieved higher accuracy［overall accuracy（OA）：92.23%；precision：0.80］than boundary extraction（OA：90.77%；precision：0.77）.Fragmentation assessment presented higher performance using the result of cropland boundary mapping compared with visual interpretation as the reference，which displayed lower difference for the comprehensive index（11.76%），as well as the subindex of area（ 8.51%） and shape（ 13.51%），which was superior to the results derived from cropland patch extraction.

Key words:  , Patch identification；Deep learning；Fragmentation assessment；Hilly region；U?Net+SE；High?resolution remote sensing；Boundary extraction