Application of BP Neural Network Model Optimized by Genetic Algorithm in Remote Sensing Water Depth Inversion
Aiming at the defects of traditional BP neural network model in water depth inversion of remote sensing images,this paper introduces Principal Component Analysis (PCA) and Genetic Algorithm (GA) to build a new GA-BP neural network model. The im-proved model uses GA to optimize the weights and thresholds of BP neural network model and takes the optimized values as the initial values of BP neural network model. The improved model is applied to the water depth inversion experiment of remote sensing images. The results show that the convergence speed of the improved model is greatly improved compared with the single BP neural network model,and the water depth inversion accuracy is also higher.
BP neural network modelprincipal component analysisgenetic algorithmwater depth inversionweight and threshold optimization
万方数据
维普
