基于MWatNet模型的河套灌区解放闸灌域灌溉水体提取

Extraction of Irrigation Water Body in Jiefangzha Irrigation Area of Hetao Irrigation District Based on MWatNet Model

摘要:

为提高灌溉农田中灌溉水体的识别精度，以河套灌区解放闸灌域作为研究区，基于Sentinel-2遥感影像，结合灌区实际情况对地表水体提取模型(WatNet)进行改进，得到MWatNet模型并提取灌溉水体。采用总体精度（Overall accuracy,OA）、平均交并比（Mean intersection over union,MIoU）、F1值等水体提取精度指标进行综合评价。结果表明：改进后的地表水体提取模型(MWatNet)在解放闸灌域农田灌溉水体的提取上具有较好的识别精度，模型总体精度达到96%，平均交并比达到83%，F1值为80%，实地调研验证准确度为85.7%；对比原WatNet、水体语义分割模型(Deeplabv3_plus)和水体提取模型(Deepwatermapv2)，MWatNet在灌溉水体提取的连结性、剔除道路和城镇干扰等方面，均表现出更好的效果和模型运行效率。利用该模型可以实现灌溉水体定量化表征，为灌溉用水调度提供了数据支撑。

Abstract:

In order to improve the recognition accuracy of irrigation water bodies in irrigated farmland, the Jiefangzha Irrigation Area of Hetao Irrigation District was taken as the study area, and the surface water body extraction model (WatNet) was improved based on Sentinel-2 remote sensing images, combined with the actual situation of the irrigation area, to obtain the MWatNet model and extract irrigation water bodies. Overall accuracy (OA), mean intersection over union (MIoU), F1 value and other water body extraction accuracy indicators were used for comprehensive evaluation. The results showed that the improved surface water body extraction model (MWatNet) had good recognition accuracy in the extraction of farmland irrigation water bodies in Jiefangzha Irrigation Area, the overall accuracy of the model reached 96%, the mean interaction over union reached 83%, the F1 value was 80%, and the accuracy of the field research validation was 85.7%;comparing with the original WatNet, the semantic segmentation model of water bodies (Deeplabv3_plus), and the water body extraction model (Deepwatermapv2), MWatNet showed better results and model operation efficiency in terms of connectivity of irrigation water body extraction, and elimination of road and town interference. The quantitative characterization of irrigation water bodies can be achieved by using this model, which provided data support for irrigation water scheduling.