A Novel Attention-Based Early Fusion Multi-Modal CNN Approach to Identify Soil Erosion Based on Unmanned Aerial Vehicle

作者：Sheng Miao, Yufeng Liu, Zitong Liu, Xiang Shen, Chao Liu, Weijun Gao · 发表于：IEEE Access · 年份：2024 · DOI：10.1109/access.2024.3425654 · 被引用次数：9 · 研究领域：Soil erosion and sediment transport、Remote Sensing and LiDAR Applications、Hydrology and Sediment Transport Processes

Soil erosion poses significant ecological and economic challenges, necessitating precise and effective identification methods. Traditional models frequently overlook the intricate relationships between erosion factors and multispectral remote sensing data. To enhance these traditional methods, a novel dual-input gated fusion Convolutional Neural Network (CNN) has been developed, integrating channel and spatial attention mechanisms. This innovative model strengthens the connection between multispectral images and erosion factors, improving the accuracy and generalizability of erosion predictions. The model utilizes data collected from unmanned aerial vehicles (UAVs) equipped with high-precision multispectral sensors. By processing both spectral images and erosion factor data, the model effectively captures complex soil spatial distributions. The dual-input gated fusion mechanism allows the network to extract high-level semantics while suppressing redundant information, ensuring robust performance even in heterogeneous terrains. Experimental results indicate that this framework significantly enhances the performance of traditional models, providing superior predictions for small and medium-sized areas. Experimental results indicate that the presented framework can achieve better accuracy (96.92%) compared with other machine learning approaches, such as Random Forest (89.64%), VGGNET (91.52%), and RESNET (90.18%). Moreover, the proposed method can improve accuracy by 26.59% comp...