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.

摘要

本文提出了一种基于卷积运算（Convolutional Seq2Seq），结合长短时记忆（CNN_LSTM）和Seq2Seq的卷积神经网络，用卷积代替Seq2Seq的全连接运算，并改进Seq2Seq的关注机制以监控变化在水体中。卷积Seq2Seq和CNN_LSTM可以提取遥感图像时间序列的时空特征。我们还建议使用降采样和分辨率恢复（DDR）模块来减少两个模型的计算资源消耗。与流行的全卷积网络（FCN）-8s，具有ResNet101基线的DeepLab v2和长短时记忆（LSTM）方法相比，基于卷积Seq2Seq和CNN_LSTM的水量变化监测结果具有更低的噪声和更高的精度。