HRS-UNet: A Semantic Segmentation Model for Precise Crop Classification in Hyperspectral Remote Sensing Image

Precise crop classification, as a pivotal technology underpinning precision agriculture, has attracted considerable attention in recent years. Hyperspectral imaging systems mounted on Unmanned Aerial Vehicles UAVs are capable of producing high spatial resolution hyperspectral imagery, offering distinct advantages including low operational costs, high operational flexibility, and real-time data acquisition. As a result, these systems have emerged as an optimal tool for precise crop classification within precision agriculture monitoring. Nevertheless, existing methods for crop classification using UAV hyperspectral imagery encounter a trade-off between global feature perception and computational complexity, frequently leading to the loss of spatial features. To tackle this issue, this study introduces a hyperspectral segmentation network, HRS-UNet, designed to achieve precise crop classification from hyperspectral samples. And we propose a Multiscale Spectral Aggregation MSA module, which greatly reduces the computational burden of the backbone network through feature enhancement and dimensionality reduction. Evaluation results on the UAV-HSI-Crop dataset reveals that our model attains state-of-the-art performance, achieving an overall classification accuracy of 89.96 and a Kappa coefficient of 0.8814, outperforming existing approaches. Our model offers a novel technical pathway for efficient monitoring in precision agriculture.