Bi-Directional Temporal Modelling for Semantic Change Detection in Remote Sensing Images

Semantic change detection (SCD) is a branch of change detection (CD) that provides detailed land-cover/land-use (LCLU) change information. It presents not only the changed information but also the bi-temporal LCLU semantic maps (in the changed areas). Studies have recently highlighted [1] that SCD can be addressed through a triple-branch Convolutional Neural Network (CNN), which contains two multi-temporal branches and a change detection branch. However, in this architecture, the two temporal branches learn insufficient LCLU transition information. In this paper, we present a novel architecture that combines CNN and RNN for the SCD of remote sensing images. It employs a Siamese CNN to learn semantic information from two temporal images, followed by a Bidirectional Recurrent Neural Network (Bi-directional RNN) to learn temporal dependencies of the LCLU classes. The resulting CNN-RNN architecture can model better the LCLU transitions, thus enhancing the semantic representation of the bi-temporal features. Experimental results on a benchmark dataset show that the proposed method obtains significant accuracy improvements over the existing approaches. It also shows advantages in recognizing the minority LCLU changes.