A NEW BP NEURAL NETWORK FUSION ALGORITHM FOR MULTI-SOURCE REMOTE SENSING DATA ON GROUNDWATER

This paper aims to enhance the accuracy and reduce the cost of the fusion of multi-source remote sensing data. For this purpose, the existing multi-source remote sensing data fusion methods were reviewed in detail. Then, a new back propagation (BP) neural network (BPNN) fusion algorithm for the groundwater was put forward based on hybrid soft computing. Using the function approximation ability of BP neural network, it was combined with the Kalman filter to form an optimization method. The BP neural network was coupled with the particle swarm optimization (PSO) algorithm into the PSO-BPNN-EKF data fusion algorithm. On this basis, the least squares support vector machine (LSSVM) was introduced to create the LSSVM-PSO data fusion algorithm. Through simulation experiments, it is learned that the proposed algorithm can effectively fuse the multi-source remote sensing data on groundwater, especially in the case of big data. The research findings shed a new light on the fusion of remote sensing data collected by multiple sensors.