基于“分区-分层”深度学习的干旱区土地利用遥感精细制图

doi:10.12118/j.issn.1000-6060.2025.732

干旱区地理 ›› 2026, Vol. 49 ›› Issue (10): 2122-2133.doi: 10.12118/j.issn.1000-6060.2025.732 cstr: 32274.14.ALG2025732

基于“分区-分层”深度学习的干旱区土地利用遥感精细制图

李锦珍¹(), 吴田军¹(), 骆剑承²^,³, 胡晓东⁴, 周忠发⁵, 曹峰⁶

¹ 长安大学土地工程学院，陕西西安 710064
² 中国科学院空天信息创新研究院遥感与数字地球全国重点实验室，北京 100101
³ 中国科学院大学资源与环境学院，北京 100049
⁴ 浙江科技大学计算机科学与技术学院，浙江杭州 310023
⁵ 贵州师范大学地理与环境科学学院/喀斯特研究院，贵州贵阳 550001
⁶ 山西大学计算机与信息技术学院，山西太原 030006

收稿日期:2025-11-17 修回日期:2026-01-09 出版日期:2026-10-25 发布日期:2026-10-26
通讯作者: 吴田军（1986-），男，博士，教授，主要从事遥感大数据智能处理分析、农业/生态遥感应用等方面的研究. E-mail: tjwu@chd.edu.cn
作者简介:李锦珍（2002-），女，硕士研究生，主要从事遥感大数据智能处理分析等方面的研究. E-mail: 2024135017@chd.edu.cn
作者贡献
论文构思：李锦珍、吴田军；数据管理：李锦珍；规范分析：李锦珍；获取资助：吴田军；调查研究：李锦珍；方法设计：李锦珍、吴田军、骆剑承；项目管理：吴田军；提供资源：吴田军、骆剑承、胡晓东；软件编程：李锦珍；监督指导：吴田军；验证复核：李锦珍；可视化：李锦珍；初稿撰写：李锦珍；修订编辑：吴田军、骆剑承、胡晓东、周忠发、曹峰。
基金资助:
国家自然科学基金(42471394);陕西省自然科学基础研究计划项目(2025JC-QYCX-035);国家重点研发计划项目(2023YFF 1304700);河北省中央引导地方科技发展资金项目(236Z0104G);中国科学院空天信息创新研究院自主部署项目(E4Z 202021F)

Fine-grained land-use remote-sensing mapping in arid regions using deep learning with “zoning-stratification”

LI Jinzhen¹(), WU Tianjun¹(), LUO Jiancheng²^,³, HU Xiaodong⁴, ZHOU Zhongfa⁵, CAO Feng⁶

¹ School of Land Engineering, Chang’an University, Xi’an 710064, Shaanxi, China
² Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
³ College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
⁴ School of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China
⁵ School of Karst Science/School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550001, Guizhou, China
⁶ School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China

Received:2025-11-17 Revised:2026-01-09 Published:2026-10-25 Online:2026-10-26

摘要/Abstract

摘要：

针对干旱区地表景观空间异质性强、地物类型混杂的特点，开展土地利用精细提取与制图对国土空间规划与生态治理具有重要意义。以新疆哈密市中心城区为研究区，基于易智瑞1 m空间分辨率遥感影像，提出一种“分区-分层”地学思想引导深度学习信息提取的土地利用制图方法，其集成了地理分区划分、地物分层提取、后处理与优化等3个创新环节。结果表明：（1） 6类地物的交并比（IoU）和F1Score（F1分数）由高到低排序为：面状水体>建筑>林草地>园地>耕地>其他，除“其他”外，其余类型地物提取的F1分数均大于0.8，表明发展的新方法具有较好适用性。（2）相较于单一模型提取，“分区-分层”策略显著提升了提取精度，其平均交并比、Kappa系数、平均F1分数分别为0.783、0.762、0.871，较前者分别提升了17.92%、9.17%、23.90%。（3）所匹配的4种模型（Deeplab v3+、HRNet、REAUNet+UAED、U-Net3+）在对应目标地物上均表现最优，进一步验证了分层提取策略的有效性。研究可为地学分析思想与前沿深度学习算法在干旱区土地利用遥感精细制图中的融合应用提供新思路。

关键词: 西北干旱区, 土地利用遥感精细制图, 地理分区, 地物分层, 深度学习

Abstract:

The pronounced spatial heterogeneity of surface landscapes and the fragmented, intricately mixed distribution of land cover types in arid regions highlight the importance of fine-grained land-use remote-sensing mapping for effective territorial spatial planning and ecological governance. This study focuses on the central urban area of Hami City, Xinjiang, and proposes an improved framework for fine-grained land-use remote-sensing mapping. The framework integrates the geoscientific “zoning-stratification” strategy with advanced deep learning algorithms, utilizing 1-m high-resolution satellite imagery from the ESRI platform. The proposed framework consists of three innovative components: Geographical zoning, stratified object extraction, and post-processing optimization. The results demonstrate the following: (1) The six land use types are ranked by Intersection over Union (IoU) and F1 score as follows: water bodies>buildings>forest and grassland>orchards>cultivated land>other. Excluding the “other” category, the F1 score for all specific land-use types exceeds 0.8, indicating strong applicability and stable performance of the method under complex arid environmental conditions. (2) The “zoning-stratification” strategy significantly enhances extraction accuracy compared to conventional single-model extraction methods. The proposed method achieves a mean IoU (mIoU) of 0.783, a Kappa coefficient of 0.762, and a Macro-F1 of 0.871, which correspond to relative improvements of 17.92%, 9.17%, and 23.90%, respectively, over the baseline method. (3) The four deep learning models used in the study, namely Deeplab v3+, HRNet, REAUNet+UAED, and U-Net3+, each demonstrate optimal performance for their respective land-use types, validating the effectiveness of the stratified object extraction strategy. In summary, this study offers a fresh perspective on integrating geoscientific analytical approaches with advanced deep learning algorithms for fine-grained land-use remote-sensing mapping in arid regions.

Key words: northwestern arid regions, fine-grained land-use remote-sensing mapping, geographical zoning, stratified object extraction, deep learning

李锦珍, 吴田军, 骆剑承, 胡晓东, 周忠发, 曹峰. 基于“分区-分层”深度学习的干旱区土地利用遥感精细制图[J]. 干旱区地理, 2026, 49(10): 2122-2133.

LI Jinzhen, WU Tianjun, LUO Jiancheng, HU Xiaodong, ZHOU Zhongfa, CAO Feng. Fine-grained land-use remote-sensing mapping in arid regions using deep learning with “zoning-stratification”[J]. Arid Land Geography, 2026, 49(10): 2122-2133.

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分层次序	地物类型	匹配模型	提取目标特征
第一层	建筑	Deeplab v3+	城镇建筑：单体占地面积大、形态规则、边界清晰且大规模空间集聚农村建筑：单体占地面积小、小规模团块状或带状分布、蓝白色调屋顶
第二层	面状水体	HRNet	纹理均质、边界清晰
第三层	耕地	REAUNet+UAED	纹理平滑、边界较清晰
第四层	园地	REAUNet+UAED	颗粒状纹理且排列规整，边界模糊
第五层	林草地	U-Net3+	天然牧草地、防护林、城市绿化带和绿化公园等亚类，纹理复杂且边界过渡模糊
第六层	其他	U-Net3+	纹理不均质、植被覆盖度低且边界模糊

地物类型	地块数量/个	地块数量占比/%	总面积/km²	面积占比/%
建筑	26893	58.00	18.76	7.83
面状水体	66	0.14	0.54	0.22
耕地	4846	10.45	47.38	19.76
园地	3227	6.96	15.86	6.62
林草地	5089	10.98	21.11	8.81
其他	6248	13.47	136.06	56.76
总计	46369	100.00	239.71	100.00

地物类型	IoU	UA	PA	F1Score
建筑	0.818	0.924	0.877	0.900
面状水体	0.915	0.979	0.935	0.956
耕地	0.730	0.854	0.820	0.837
园地	0.752	0.868	0.843	0.855
林草地	0.793	0.921	0.852	0.885
其他	0.690	0.819	0.766	0.792

地物类型	精度指标	模型
地物类型	精度指标	Deeplab v3+	HRNet	REAUNet+UAED	U-Net3+
建筑	UA	0.887	0.795	0.734	0.798
	PA	0.839	0.743	0.687	0.763
	F1Score	0.862	0.768	0.710	0.780
面状水体	UA	0.868	0.895	0.801	0.731
	PA	0.811	0.846	0.768	0.697
	F1Score	0.838	0.870	0.784	0.714
耕地	UA	0.821	0.712	0.831	0.669
	PA	0.798	0.754	0.796	0.925
	F1Score	0.809	0.732	0.813	0.777
园地	UA	0.643	0.673	0.822	0.764
	PA	0.929	0.916	0.830	0.705
	F1Score	0.761	0.776	0.826	0.733
林草地	UA	0.741	0.749	0.758	0.820
	PA	0.736	0.779	0.807	0.888
	F1Score	0.738	0.764	0.782	0.853
其他	UA	0.721	0.709	0.664	0.703
	PA	0.750	0.742	0.638	0.861
	F1Score	0.735	0.725	0.650	0.774

基于“分区-分层”深度学习的干旱区土地利用遥感精细制图

Fine-grained land-use remote-sensing mapping in arid regions using deep learning with “zoning-stratification”

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图/表 12

参考文献 32

相关文章 15

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地物类型	样本数量/个	图斑数量/个
建筑	17	8305
面状水体	16	40
耕地	79	9636
园地	56	4967
林草地	85	2795
其他	57	398

方法	mIoU	Kappa系数	Macro-F1
单一模型提取	0.664	0.698	0.703
“分区-分层”提取	0.783	0.762	0.871

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