Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery
Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery
Blog Article
Suspended solids concentration (SSC) is an important indicator of the degree of water pollution.However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient.Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC.A reservoir and a polluted riverway were selected as the study areas.
The spectral data of Fan Shop - Licensed Baseball - Novelty the 36-point and 29-point 400−900 nm wavelength range on the UAV-borne images were extracted.Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved.The training samples had a coefficient of determination (
68 mg/L, and a Brides mean absolute percentage error (MAPE) of 12.66% at the reservoir.For the polluted riverway, PSO-LSSVM also performed well.Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images.
The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.