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SenFus-CHCNet: a multi-resolution fusion framework for sparse-supervised canopy height classification
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DOI：10.3389/frsen.2025.1666123.png)
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Abstract
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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.
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