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

ABSTRACT

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.

Acknowledgments

The authors would like to acknowledge the National Key Research and Development of China. The author would also like to acknowledge the developers in the GDAL, PYTORCH, OPENCV for their open-source projects.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported in part by the funding from National Key Research and Development Program of China [No. 2017YFB0504203].