A Recurrent Convolutional Neural Network for Land Cover Change Detection in Multispectral Images

In this paper, we propose a novel network architecture, a recurrent convolutional neural network, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection of multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations while the latter effectively analyzes temporal dependency in bi-temporal images. Although both CNN and RNN are well-established techniques for remote sensing applications, to the best of our knowledge, we are the first to combine them for multitemporal data analysis in the remote sensing community. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode.