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

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.