Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Abstract

Background As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.

Spatio-temporal data prediction of multiple air pollutants in multi-cities based on 4D digraph convolutional neural network

“…However, the practical inter-city relationships of atmospheric pollutants contain two-dimensional spatial relationships, including longitude and latitude, and exhibit a certain degree of directionality in pollutant diffusion. A directed graph containing two-dimensional spatial information should describe the spatial correlations among cities, while considering the multi-factor correlations and temporal dependencies of various atmospheric pollutants [33].…”