基于压缩感知图像分析的河流表面流速估计方法

Estimation of river surface flow velocity through image analysis based on compressed sensing

提出一种新的非接触式河流表面流速估计方法。以水流图像的特征识别为基本原理,通过图像采集和预处理、对应类别标签与流速关系映射表的建立及数据分析等技术方法的集成,建立一套河流表面流速估计方案。选择位于湖北省通山县境内九宫梯级水电站所在的宝石河二级支流——界牌河作为实验对象,利用所提特征约束组稀疏分类器(GSCFC)进行数据分析得到表面流速范围,实验验证了压缩感知图像分析方法用于河流表面流速估计的可行性。此外,将GSCFC与经典的稀疏表示分类器(SRC)和抗噪性正则化编码分类器(RRC)进行对比可得,GSCFC对于水流图像具有鲁棒的鉴别力,且其综合性能明显优于SRC和RRC。

Based on the fundamental principle of feature recognition of water flow images, an emerging non-contact river surface flow velocity estimation method is developed in this study by integrating image acquisition, image pre-processing, implicit mapping between class labels and flow velocity, and data analysis. This method is verified using a group sparse representation classier with feature constraints（GSCFC）, a new parameter introduced by the authors for image analysis and estimation of the surface flow velocity of the Jiepai River. And we evaluate GSCFC by conducting comparative experiments with the classical sparse representation based classifier（SRC） and the regularized robust coding classifier（RRC）. Results show that GSCFC is a robust discriminative classifier that has an excellent performance for water flow image and outperforms SRC and RRC.