Change detection is the study of detecting changes between two different images of a scene taken at different times. This paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. The problem to be solved consists of two parts, semantic segmentation and change detection. 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, 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.
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