Analysis of surface temperature characteristics of multiscale fusion based on convolution neural network

School of geodesy and geomatics, Wuhan University, 430072 Wuhan, China

Abstract

Intelligent detection of surface temperature 1, describing macro characteristics of microcosmic combination, promotes the cross fusion of geoscience, thermodynamics, climatology, geological science, and so on. However, there are still two notable problems to be solved. One is the model lacks characterization capability, and the other is that the precision of surface temperature’s monitoring and prediction is low. To solve these problems, we propose an algorithm to predict surface temperature characteristics of multiscale fusion based on convolution neural network. Firstly, after researching the multiscale disturbance characteristics of surface temperature, we draw a conclusion based on analyzing time change, spatial change, casual change. To improve the parameter correlations among surface temperature characteristics, a neural network about compensating and optimizing analysis of surface temperature characteristics is proposed on the fundamental of multivariate surface temperature characterization models. By designing cluster input layer, dynamic hidden layer and visual output layer of neural network, the precise of predict data has been improved by 53.3% on average, and 76.0% on variance compared with remote sensing data. What’s more, the data consumption of this model has promoted by 17.2% in contrast to grey theory on predictive complexity and precision, and 10.8% compared with BP neural network.