用影像和激光点云融合提升森林生物多样性评估精度
Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds
- 融合2D影像与3D点云数据，采用深度学习进行多模态分析
- 端到端训练模型达到82.0%准确率，优于单一模态
- 适合生态监测、林业管理等需要高效评估的场景
森林生物多样性评估对生态系统管理和保护至关重要。传统野外调查虽精度高，但耗时且空间覆盖有限。本研究探讨基于深度学习的近距传感数据融合——2D正射影像与3D机载激光扫描（ALS）点云——是否可有效评估森林生物多样性潜力。我们构建了BioVista数据集，包含来自丹麦温带森林的44,378组配对样本，用于探索多模态融合方法。使用ResNet处理影像、PointVector处理点云，单一模态分别实现76.7%和75.8%的总体准确率。对比多种融合策略：基于置信度的集成、特征级拼接及端到端训练，其中端到端方法在区分低/高潜力林区时达到82.0%准确率。结果表明，影像光谱信息与点云结构信息在评估森林生物多样性潜力方面具有显著互补性。
原文摘要 · Abstract (English)
Assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos and 3D airborne laser scanning (ALS) point clouds can reliable assess the biodiversity potential of forests. We introduce the BioVista dataset, comprising 44378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multimodal fusion approaches. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving overall accuracies of 76.7% and 75.8%, respectively. We explore various 2D and 3D fusion approaches: confidence-based ensembling, feature-level concatenation, and end-to-end training, with the latter achieving an overall accuracies of 82.0% when separating low- and high potential forest areas. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in the assessment of forest biodiversity potential.
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