微型电脑应用

监测城市河道和湖泊的水质是确保环境可持续发展和公共健康的关键措施，对此，提出一种结合无人机(UAV)高光谱遥感和卷积神经网络(CNN)技术的河湖水质动态监测方法。利用UAV搭载的高光谱遥感传感器获取水体影像数据，包括悬浮固体、总氮、总磷、氨氮、化学需氧量等关键水质参数。采用CNN对水体影像进行目标检测和分割，实现对河湖区域的自动识别，提取水质参数特征进行反演，从而实现对水质参数的动态监测。测试阶段，使用采集数据对CNN进行训练，并评估所提方法在水质监测中的有效性和准确性，实验结果表明了所提方法在河湖水质监测中的优越性。

Monitoring the water quality of urban rivers and lakes is a key measure to ensure environmental sustainability and public health. This paper proposes a dynamic water quality monitoring method for rivers and lakes by combining unmanned aerial vehicle(UAV) hyperspectral remote sensing and convolutional neural network(CNN) technologies. Hyperspectral remote sensing sensors mounted on UAVs are used to acquire water body image data, including key water quality parameters such as suspended solids, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand. The CNN is used to perform object detection and segmentation on the water body images, enabling automatic identification of river and lake areas, extracting water quality parameter features for inversion, and thus achieving dynamic monitoring of water quality parameters. During the testing phase, the collected data are used to train the CNN, and the effectiveness and accuracy of the proposed method in water quality monitoring are evaluated. The experimental results demonstrate the superiority of the proposed method in monitoring river and lake water quality.