DATerra: Boundary and structure aware domain adaptation for cross-regional terraced parcel extraction

IF 8.2 Q1 REMOTE SENSING
Zhiwei Zhang, Xingyuan Zeng, Xinkai Kong, Juepeng Zheng, Haoyuan Liang, Guowei Zhou, Bohan Shi, Lingyuan Zhao, Jianxi Huang, Haohuan Fu
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引用次数: 0

Abstract

Irregular agricultural parcels, particularly terraced fields, exhibit complex geometries and fine-scale boundaries that are highly sensitive to cross-regional variations in imaging conditions and landscape patterns. Although unsupervised domain adaptation (UDA) has shown promise for remote sensing segmentation under domain shifts, most existing methods primarily focus on semantic alignment while failing to adequately preserve boundary geometry and parcel-level structural consistency, often leading to boundary adhesion and distorted parcel delineation in target domains. To address these limitations, we propose DATerra, a boundary and structure aware domain adaptation framework for cross-regional terraced parcel extraction. The framework couples two architecture-level boundary-modeling modules with two training-level structure-preserving strategies: (i) Boundary-Guided Attention Fusion (BGAF), which injects geometry-aware cues into multi-scale feature learning; (ii) Boundary-aware Uncertainty-guided Gating (BUDG), which selectively refines ambiguous boundary regions while preserving reliable parcel interiors; and (iii) Parcel Structural Consistency (PSC), together with Structure-Aware ClassMix (SA-ClassMix), which promotes stable parcel predictions under partial target-domain observations and preserves parcel–boundary structural units during cross-domain mixing. Extensive experiments on the GTPBD-China benchmark, covering four geographic regions and twelve cross-domain transfer scenarios, demonstrate that DATerra substantially improves boundary delineation and object-level parcel integrity. On average, DATerra improves OIS and ODS by 14.00 and 10.76 percentage points, respectively, while reducing GUC and GTC by 20.14 and 9.98 points compared with the runner-up methods. Pixel-level metrics are also consistently improved, with an average gain of 1.23%. These results highlight the importance of explicitly modeling boundary geometry and structural consistency for robust cross-regional terraced parcel extraction.
数据:边界和结构感知域自适应跨区域梯田地块提取
不规则的农业地块,特别是梯田,呈现出复杂的几何形状和精细尺度的边界,对成像条件和景观模式的跨区域变化高度敏感。尽管无监督域自适应(UDA)在域移动下的遥感分割中显示出前景,但大多数现有方法主要关注语义对齐,而未能充分保持边界几何形状和包级结构一致性,往往导致目标域的边界粘附和包圈定失真。为了解决这些限制,我们提出了DATerra,这是一个跨区域梯田包裹提取的边界和结构感知域适应框架。该框架将两个架构级边界建模模块与两种训练级结构保持策略相结合:(i)边界引导注意融合(BGAF),将几何感知线索注入到多尺度特征学习中;(ii)边界感知不确定性引导门控(BUDG),它有选择地细化模糊的边界区域,同时保留可靠的包裹内部;(iii)包裹结构一致性(PSC),以及结构感知类混合(SA-ClassMix),在部分目标域观测下促进稳定的包裹预测,并在跨域混合期间保留包裹边界结构单元。在GTPBD-China基准上,覆盖4个地理区域和12个跨域迁移场景的大量实验表明,DATerra显著提高了边界划分和目标级包裹完整性。与排名第二的方法相比,DATerra的OIS和ODS平均分别提高了14.00和10.76个百分点,GUC和GTC平均分别降低了20.14和9.98个百分点。像素级指标也得到了持续改进,平均增益为1.23%。这些结果强调了明确建模边界几何和结构一致性对于鲁棒跨区域梯田包裹提取的重要性。
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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