Water Quality Retrieval and Performance Analysis Using Landsat Thermatic Mapper Imagery Based on LS-SVM

Because of the limited number of monitoring points on the ground, the accuracy of traditional monitoring methods using remote sensing was lower. This paper proposed to use the Least Squares Support Vector Machine (LS-SVM) theory to improve the accuracy of water quality retrieval, which is suitable for the small-sample fitting. The Radial Basic Function (RBF) was chosen as the kernel function of the retrieval model, and the grid searching and k-cross validation were used to choose and optimize the parameters. This paper made use of the LS-SVM model and some traditional retrieval models to retrieve concentration of suspended matter. Comparing the results of experiment, it showed that the proposed method had good performance and at the same time, the complexity is lower and the speed of the modeling was rapid.