A Deep Siamese Postclassification Fusion Network for Semantic Change Detection

Abstract

Semantic change detection (SCD) aims to recognize land cover transitions from remote sensing images of the given scene acquired at different times. The semantic change maps produced by SCD can provide not only the locations of changes but also the detailed change types (e.g., “from-to” change type). This exhaustive change information plays a significant role in various applications. Postclassification methods with multitemporal remote sensing images have been widely used in SCD. However, many existing methods suffer from the accumulation of misclassification errors. In this article, a deep Siamese postclassification fusion network (PCFN) is proposed to address this problem. PCFN is composed of the Siamese classification network (SCN) for land cover mapping (LCM) and soft fusion network (SFN) for postclassification SCD, respectively. In PCFN, SCN is designed to effectively integrate the temporal correlation between two images by processing the joint features, further improving classification performance. Then, SFN is constructed to determine changes and identify the specific change types through automatic soft fusion. SFN fuses the multitemporal LCM features generated by SCN, then adaptively maps them to the decision space for soft fusion during network training, and predicts the final semantic change maps. Extensive experimental results on two challenging SCD datasets demonstrate that our method can alleviate the error accumulation effectively by combining temporal correlation integration and soft fusion, and achieve promising performance superior to the other state-of-the-art methods in SCD.