Fine-Scale Grassland Classification Using UAV-Based Multi-Sensor Image Fusion and Deep Learning

作者：Zhongquan Cai, Changji Wen, Lun Bao, Hongyuan Ma, Zhuoran Yan, Jiaxuan Li, Xiaohong Gao, Lingxue Yu · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17183190 · 被引用次数：6 · 研究领域：Advanced Image Fusion Techniques、Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture

Grassland classification via remote sensing is essential for ecosystem monitoring and precision management, yet conventional satellite-based approaches are fundamentally constrained by coarse spatial resolution. To overcome this limitation, we harness high-resolution UAV multi-sensor data, integrating multi-scale image fusion with deep learning to achieve fine-scale grassland classification that satellites cannot provide. First, four categories of UAV data, including RGB, multispectral, thermal infrared, and LiDAR point cloud, were collected, and a fused image tensor consisting of 10 channels (NDVI, VCI, CHM, etc.) was constructed through orthorectification and resampling. For feature-level fusion, four deep fusion networks were designed. Among them, the MultiScale Pyramid Fusion Network, utilizing a pyramid pooling module, effectively integrated spectral and structural features, achieving optimal performance in all six image fusion evaluation metrics, including information entropy (6.84), spatial frequency (15.56), and mean gradient (12.54). Subsequently, training and validation datasets were constructed by integrating visual interpretation samples. Four backbone networks, including UNet++, DeepLabV3+, PSPNet, and FPN, were employed, and attention modules (SE, ECA, and CBAM) were introduced separately to form 12 model combinations. Results indicated that the UNet++ network combined with the SE attention module achieved the best segmentation performance on the validation set,...