农村黑臭水体类型多，成因复杂，严重影响生态环境质量，使用遥感影像开展农村黑臭水体监测具有实际意义。然而，浮萍型农村黑臭水体与部分植被、绿色房顶和大棚等存在异物同谱现象，利用色度法、特征光谱法提取效果均不理想，严重阻碍了浮萍型农村黑臭水体的高精度、重复性和自动化监测。针对该问题，实验利用GF1、GF2、GF6遥感影像，收集和解译了覆盖西安市多个区县、涉及多个污染对象的299处浮萍型农村黑臭水体信息，基于DeeplabV3+语义分割神经网络，采用ResNet101为骨干网络，引入ECA（Efficient Channel Attention）注意力机制，配合对低照度样本开展提亮和纠色前处理，搭建了浮萍型黑臭水体语义分割模型。该模型的F1-score为0.947，平均交并比 （MIoU， Mean Intersection over Union）为0.948，浮萍型黑臭水体的交并比（IoU， Intersection over Union）为0.884，错误漏检率（FOR， False Omission Rate）为0.081，表明该模型具备从高分辨率遥感影像上高效、精准、重复识别浮萍型农村黑臭水体的能力，可以为政府部门监管农村黑臭水体提供抓手。

As black and odorous water bodies in rural areas have negative influence to environment， it is important to monitor the rural black and odorous water bodies by high resolution remote sensing. While， the spectral curve from remote sensing of rural black and odorous is similar to some vegetation， green roofs and greenhouses， which bring difficulties to identify the rural black and odorous in remote sensing images with satisfactory repeatability and accuracy， and automation， by using the color purity on a Commission Internationale de L’Eclairage （CIE） model and spectroscopic method. Thus， we collected and interpreted 325 rural black and odorous water bodies by GF1/2/6， covering several counties in Xi’an and including various type of polluted object， to train the model using DeeplabV3+ with ResNet101 as the backbone to identify the rural black and odorous water bodies， in which we imported the Efficient Channel Attention （ECA） and pre-processed the samples by increasing the brightness and correcting the color difference. The F1-score， MIoU （Mean Intersection over Union）， IoU （Intersection over Union） and FOR （False Omission Rate） of the model were 0.931， 0.935， 0.935 and 0.085 respectively， which indicated that the model could efficiently， accurately， and repeatedly identify rural black and odorous water bodies from high-resolution remote sensing images and offer assistance for government departments to regulate rural black and odorous water bodies.