Synergizing BRDF correction and deep learning for enhanced crop classification in GF-1 WFV imagery - Chen, Yuanwei; Li, Yang; Li, Runze; Guo, Chongzheng; Li, Jilin (Frontiers in Remote Sensing, 2025)
Accurate crop classification is essential for agricultural management, resource allocation, and food security monitoring. GF-1 Wide Field View (WFV) imagery suffers from Bidirectional Reflectance Distribution Function (BRDF) effects due to large viewing angles (0°–48°), reducing crop classification accuracy. This study innovatively integrates BRDF correction with deep learning to address this. First, a BRDF correction method based on normalized difference vegetation index (NDVI) and anisotropy flat index (AFX) is developed to normalize radiometric discrepancies. Secondly, utilizing four spectral bands from WFV images along with three effective vegetation indices as feature variables, a multi-feature fusion deep learning classification system was constructed. Three typical deep learning architectures—Feature Pyramid Network (FPN), Fully Convolutional Network (FCN), and UNet, are employed to perform classification experiments. Results demonstrate that BRDF correction consistently improves accuracy across models, with UNet achieving the best performance: 95.02% overall accuracy (+0.65%), 0.9316 Kappa (+0.0088), and 91.29% mean IOU (+1.06%). The improved classification accuracy of mIoU (+2.31%) of FPN and OA (+2.11%) of FCN proves the necessity of BRDF correction. By integrating physical BRDF correction with deep learning techniques, this study establishes a new benchmark for precision crop mapping in large-viewing satellite imagery, thereby advancing scalable solutions for agricultural monitoring.