Semantic Change Detection Based on a New Chinese Satellite Dataset and a Deep Conditional Random Field Framework

Sunan Shi, Yanfei Zhong, Yinhe Liu, Jue Wang, Deren Li

Abstract: In this paper, a new semantic change detection (CD) dataset based on Chinese Gaofen-2 (GF-2) satellite images with high spatial resolution (HSR) namely Wuhan Urban Semantic Understanding (WUSU) dataset is built up and a CD framework combining binary and semantic CD tasks based on deep learning and a conditional random field model (SDCRF) is proposed. Existing CD datasets mostly focus on “change/no change”. Traditional CD methods pay attention only on either of the binary CD task or the semantic CD task. Although there are methods to handle both tasks simultaneously but they ignore the inconsistency between the two tasks. In the SDCRF framework, any state-of-the-art feature extraction model can be used to extract the class and change probabilities as the unary potential of a fully connected conditional random field (FC-CRF) model which is adopted as a post-processing to enhance the location information of deep networks and reduce outlier noise.