Semantic Change Detection

Change detection is the study of detecting changes between two different images of a scene taken at different times. The change detection methodology can provide us information in which area images changed time by time. However, for application use, especially on disaster investigation, it is highly required to understand not only where but also what changes are occured in high precision and resolution. The paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. We mainly focus on the novel semantic segmentation in addition to a conventional change detection approach. In order to solve this problem and obtain a high-level of performance, we propose an improvement to the hypercolumns representation, hereafter known as hypermaps, which effectively uses convolutional maps obtained from convolutional neural networks (CNNs). We also employ multi-scale feature representation captured by different image patches. We applied our method to the TSUNAMI panoramic change detection dataset (TSUNAMI dataset), and re-annotated the changed areas of the dataset via semantic classes. The results show that our multi-scale hypermaps provided outstanding performance on the re-annotated TSUNAMI dataset.