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.
翻译:语义变化检测( SCD) 延伸了多级变化检测( MCD) 任务, 不仅提供变化位置, 也提供观测间隔前后的详细土地覆盖/ 土地利用( LCLU) 类别。 这种细微的语义变化信息在许多应用中非常有用。 最近的研究显示, SCD 可以通过一个包含两个时间分支和一个变化分支的三分关系革命神经网络( CNN) 来建模。 但是, 在这个结构中, 时间分支和变化分支之间的沟通不够充分。 为了克服现有方法的局限性, 我们提议为 SCD 建立一个新型CNN 结构, 将语义时间特征合并到一个深的 CD 单元中。 此外, 我们详细阐述这一结构是为了说明双时语义的语义关联性关系。 由此产生的双时序语义解释网络( Bi-SRNet) 包含两类语义推理推理学推理块块块, 以解释单一时空和跨时序的关联性关系。 为了克服现有方法的限制, 我们建议 SCD 将新的丢失功能结构图理理学分界线段结构的改进过程。 。 实验性结构在测试中将获得显著的校正标改进 。