河南农业科学2026，Vol.55Issue(9)：172-180,9.DOI:10.15933/j.cnki.1004-3268.2026.09.016
基于深度学习的丘陵区耕地田块识别与细碎化程度评价
Identification of Cropland Parcels and Evaluation of Cropland Fragmentation Degree in Hilly Areas Based on Deep Learning
摘要
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.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Key words
Patch identification/Deep learning/Fragmentation assessment/Hilly region/U-Net+SE/High-resolution remote sensing/Boundary extraction分类
农业科技引用本文复制引用
徐浩伦,韩胜斌,张建勇,李亚龙,高艺,程维东..基于深度学习的丘陵区耕地田块识别与细碎化程度评价[J].河南农业科学,2026,55(9):172-180,9.基金项目
自然资源部数字制图与国土信息应用重点实验室开放课题(ZRZYBWD202204) （ZRZYBWD202204）
国家自然科学基金项目(42401541) （42401541）
