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基于深度学习的黑臭水体遥感信息提取模型
引用本文:邵琥翔,丁凤,杨健,郑子铖.基于深度学习的黑臭水体遥感信息提取模型[J].长江科学院院报,2022,39(4):156-162.
作者姓名:邵琥翔  丁凤  杨健  郑子铖
作者单位:1.福建师范大学 福建省陆地灾害监测评估工程技术研究中心,福州 350007;2.福建师范大学 地理科学学院,福州 350007;3.中国科学院 空天信息创新研究院,北京 100094
基金项目:民用航天“十三五”预研技术项目(y930060k8m);;福建省自然科学基金项目(2017J01463);
摘    要:黑臭水体分布广泛,严重损害人民的居住环境和城市整体美观形象。以河北省廊坊市为研究区,利用高分二号(GF-2)多光谱数据和实测数据,使用PSPNet(Pyramid Scene Parsing Network)和U-Net模型对黑臭水体遥感信息提取进行对比实验研究。基于可见光波段(RGB)及近红外波段(NIR)计算归一化差异植被指数(NDVI)和归一化差异黑臭水体指数(NDBWI),针对细小形状的黑臭水体普遍存在的漏检问题,引入注意力机制模块对模型进行优化改进,构建改进型深度学习黑臭水体遥感信息提取模型。结果表明:输入RGB+NIR+NDVI+NDBW六通道组合遥感影像并引入注意力机制的U-Net网络模型对黑臭水体的提取结果最佳,其精度评价指标F1-srore、MIoU、Recall分别达到了0.864 5、0.868 1、0.835 9。

关 键 词:黑臭水体  深度学习模型  PSPNet网络模型  U-Net网络模型  GF-2卫星  遥感信息  注意力机制  
收稿时间:2021-01-18
修稿时间:2022-03-03

Model of Extracting Remotely-sensed Information of Black and Odorous Water Based on Deep Learning
SHAO Hu-xiang,DING Feng,YANG Jian,ZHENG Zi-cheng.Model of Extracting Remotely-sensed Information of Black and Odorous Water Based on Deep Learning[J].Journal of Yangtze River Scientific Research Institute,2022,39(4):156-162.
Authors:SHAO Hu-xiang  DING Feng  YANG Jian  ZHENG Zi-cheng
Affiliation:1. Fujian Engineering Technology Research Center of Land Disaster Monitoring and Evaluation, Fujian Normal University, Fuzhou 350007, China;2. College of Geography, Fujian Normal University, Fuzhou 350007, China;3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Abstract:Black and odorous water bodies are widely distributed and seriously damages people’s living environment and the overall beautiful image of the city. With Langfang City in Hebei Province as the research area, we conducted a comparative experimental study on the extraction of remote sensing information of black and odorous water bodies by using PSPNet (Pyramid Scene Parsing Network) and U-Net model based on the multi spectral data and measured data of GF-2 satellite. The normalized difference vegetation index (NDVI) and normalized difference black odorous water index (NDBWI) are calculated based on the visible light band (RGB) and near infrared band (NIR). In view of the common problem of missing detection of small black and odorous water bodies, the module of attention mechanism is introduced to optimize and improve the model. Thus, an improved deep learning model of extracting the remotely-sensed information of black and odorous water bodies is constructed. Results demonstrate that the U-Net model, which inputs RGB+NIR+NDVI+NDBWI six channel remote sensing images in combination with attention mechanism, has the most superior extraction result for black and odorous water bodies, and achieves 0.864 5, 0.868 1 and 0.835 9 in accuracy evaluation indexes F1-srore, MIoU and Recall, respectively.
Keywords:black and odorous water  deep learning model  PSPNet model  U-Net network  GF-2 satellite  remotely-sensed information  attention mechanism  
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