A method of water change monitoring in remote image time series based on long short time memory

This paper proposes convolutional neural network jointed with long short-time memory (CNN_LSTM) and Seq2Seq based on convolutional operation (Convolutional Seq2Seq), which the fully connected operation of Seq2Seq is replaced by convolution, and the attention mechanism of Seq2Seq is improved to monitor changes in water bodies. Convolutional Seq2Seq and CNN_LSTM can extract the temporal and spatial characteristics of remote sensing image time series. We also propose downsampling and resolution recovery (DDR) modules to reduce the computational resource consumption of the two models. Compared with the popular full convolutional network (FCN) -8s, DeepLab v2 with a baseline of ResNet101, and long short time memory (LSTM) methods, the water change monitoring results based on Convolutional Seq2Seq and CNN_LSTM have lower noise and higher accuracy. The CNN_LSTM method also allows fewer hidden layer features of LSTM with high-precision change monitoring results.

Automated summary

This paper introduces a Convolutional Neural Network jointed with Long Short-Term Memory (CNN_LSTM) and a Seq2Seq based on convolutional operation (Convolutional Seq2Seq) for monitoring water body changes, improving accuracy and reducing noise by extracting temporal and spatial characteristics of remote sensing image time series.