  Semantic change detection (SCD) extends the multi-class change detection
(MCD) task to provide not only the change locations but also the detailed
land-cover/land-use (LCLU) categories before and after the observation
intervals. This fine-grained semantic change information is very useful in many
applications. Recent studies indicate that the SCD can be modeled through a
triple-branch Convolutional Neural Network (CNN), which contains two temporal
branches and a change branch. However, in this architecture, the communications
between the temporal branches and the change branch are insufficient. To
overcome the limitations in existing methods, we propose a novel CNN
architecture for the SCD, where the semantic temporal features are merged in a
deep CD unit. Furthermore, we elaborate on this architecture to reason the
bi-temporal semantic correlations. The resulting Bi-temporal Semantic Reasoning
Network (Bi-SRNet) contains two types of semantic reasoning blocks to reason
both single-temporal and cross-temporal semantic correlations, as well as a
novel loss function to improve the semantic consistency of change detection
results. Experimental results on a benchmark dataset show that the proposed
architecture obtains significant accuracy improvements over the existing
approaches, while the added designs in the Bi-SRNet further improves the
segmentation of both semantic categories and the changed areas. The codes in
this paper are accessible at: github.com/ggsDing/Bi-SRNet.
