基于特征优化的SAR图像水华识别方法

Algal Bloom Discrimination Method Using SAR Image Based on Feature Optimization Algorithm

内陆湖泊水华现象的频繁爆发,严重影响着地表水环境安全,严重阻碍了我国的生态文明建设。充分发挥合成孔径雷达(Synthetic Aperture Radar,SAR)遥感技术全天时、全天候的优势,可实现大尺度、周期性的水华识别与监测工作,对于地表水生态环境的保护与监管具有重大的现实意义。立足于SAR遥感目标识别技术的研究与应用,文中提出了一种基于特征优化的水华识别方法。该方法基于对水华SAR图像特征的深入分析与提取,应用ReliefF特征优化算法对全部的22个水华特征进行筛选与优化,得到包含10个特征的最优特征子集,并以反向传播(Back Propagation,BP)神经网络为分类识别器完成了多组对比实验,水华识别总体精度最高达81.39%,较优化之前提升了19.38%。实验结果表明,使用最优特征集不仅可以大幅降低算法复杂度,还可以有效地提升水华总体识别精度,具有进一步推广的实用价值。

The frequent outbreak of algal bloom in inland lakes has seriously affected the safety of surface water environment,and has brought great obstacles to the construction of ecological civilization in China.Taking full advantage of SAR(Synthetic Aperture Radar)remote sensing technologies,large-scale and periodic algal bloom discrimination and monitoring can be realized.It is of great practical significance for the protection and supervision of water environment.Based on the research and application of SAR remote sensing target recognition technology,this paper proposes an algal bloom discrimination method with feature optimization.After the in-depth analysis and extraction of algal bloom image features,the ReliefF algorithm is used to obtain the optimal feature set,which consists of 10 features from all 22 algal bloom features.And then,the BP(Back Propagation)neural network is as the classifier of this discrimination method to carry out a number of comparative experiments.The overall accuracy of the proposed method is 81.39%,which is 19.38%higher than that before optimization.The experimental results show that the optimal feature set can not only greatly reduce the algorithm complexity,but also effectively improve the discrimination accuracy of algal bloom,which has practical value for further promotion.