Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China

Water quality assessment is essential for effective environmental management, yet traditional methods such as chemical sampling are often labor-intensive and inefficient for large-scale, continual monitoring. This study addresses these limitations by leveraging hyperspectral images (HSIs) analysis and introducing a capsule network (CapsNet) model enhanced with a multidimensional integration attention (MDIA) mechanism. The model is specifically designed to integrate both channel and spatial information, enabling precise water quality grade assessment by detecting subtle features within HSIs data. To validate the performance of the model, spectral data from 5 water quality regions are collected and processed via a UAV-carried spectrometer, with 4503 water quality data samples. Rigorous classification experiments demonstrated that the model achieves 98.73 % accuracy, with an average improvement of 4.89 % compared with the other models. This approach significantly improves decision support systems for water resource management, facilitating the sustainable use of water resources. • Used UAV-carried spectrometer for water quality HSI data. • Propose HybridNet, a capsule network model that integrates multi-dimensional attention. • Water quality can be rapidly assessed without direct contact with water samples. • The HybidNet model enhances water quaity classification performance.