Geometry-Guided Semi-Supervised Multimodal Segmentation for UAV-Based Rice-Lodging Mapping

Abstract

Accurate rice-lodging mapping from unmanned aerial vehicle (UAV) imagery supports post-disaster loss assessment, crop insurance, and precision field management. Existing deep-learning methods typically require dense pixel-level annotations, which are costly and time-consuming to produce. Moreover, RGB imagery alone often fails to distinguish lodged from healthy rice when their canopy colors and textures are similar. To address these challenges, we propose Geometry-Guided UniMatch (GUMatch), a semi-supervised multimodal segmentation framework that leverages registered RGB imagery and UAV-derived digital surface models (DSMs). Unlike conventional approaches that treat DSMs as uniformly fused auxiliary channels, GUMatch uses them as reliability-aware geometric priors, incorporating height and boundary evidence to guide lodging segmentation. Our framework integrates three key components. First, Adaptive Geometric Prompting (AGP) injects DSM-based prompt features into the decoder based on local geometric reliability and RGB–DSM compatibility. Second, Geometry-Calibrated Pseudo-Label Learning (GPL) down-weights uncertain pseudo-label supervision within teacher-identified boundary-risk regions. Third, Boundary-Aware Geometric Regularization (BGR) refines boundary localization exclusively where pseudo-labels and geometric evidence are jointly reliable. Experiments are conducted on a three-parcel UAV rice-lodging collection. The main semi-supervised benchmark trains and selects models on the Huai’an parcel HA-P2 under labeled ratios of 10%, 20%, and 40%, and evaluates them on the held-out HA-P1 parcel. The external Wuxi parcel WX-P3 is reserved solely for direct cross-region testing. With an RN-101 backbone, GUMatch achieves 80.12%, 82.98%, and 85.06% mIoU under the three labeled ratios, consistently outperforming representative semi-supervised baselines including UniMatch V2 and RSProtoSemiSeg. With a DINOv2-B backbone, GUMatch further reaches 82.64%, 84.95%, and 86.72% mIoU. On WX-P3 under the 40% labeled setting with models trained on HA-P2, GUMatch with DINOv2-B achieves 68.34% mIoU, improving over UniMatch V2 by 5.39 points. These results demonstrate that reliability-aware geometric guidance enhances annotation-efficient UAV-based rice-lodging mapping.