SenFus-CHCNet: a multi-resolution fusion framework for sparse-supervised canopy height classification - Bui-Quoc, Bao; Nguyen-Vi, Khang; Vu-Duc, Anh; Kamel, Nidal (Frontiers in Remote Sensing, 2025)
IntroductionAccurate forest canopy height mapping is critical for understanding ecosystem structure, monitoring biodiversity, and supporting climate change mitigation strategies.MethodsIn this paper, we present SenFus-CHCNet, a novel deep learning architecture designed to produce high-resolution canopy height classification maps by fusing multispectral (Sentinel-2) and synthetic aperture radar (SAR) (Sentinel-1) imagery with GEDI LiDAR data. The proposed model comprises two main components: a Multi-source and Multi-band Fusion Module that effectively integrates data of varying spatial resolutions through resolution-aware embedding and aggregation, and a Pixel-wise Classification Module based on a customized U-Net architecture optimized for sparse supervision. To discretize continuous canopy height values, we evaluate three classification schemes—coarse, medium, and fine-grained—each balancing ecological interpretability with model learning efficiency.ResultsExtensive experiments conducted over complex forested landscapes in northern Vietnam demonstrate that SenFus-CHCNet outperforms state-of-the-art baselines, including both convolutional and transformer-based models, achieving up to 4.5% improvement in relaxed accuracy (RA±1) and 10% gain in F1-score. Qualitative evaluations confirm that the predicted maps preserve fine-scale structural detail and ecologically meaningful spatial patterns, even in regions with sparse GEDI coverage.DiscussionOur findings highlight the effectiveness of deep fusion learning for canopy height estimation, particularly in resource-limited settings. SenFus-CHCNet provides a scalable and interpretable approach for forest monitoring at regional and national scales, with promising implications for biodiversity conservation, carbon accounting, and land-use planning.