Learning Deep Relationship for Image Change Detection

Very high resolution image change detection is difficult due to the low interclass variability and the resulting high overlap between the changed class and the unchanged class. To address the above difficulties, the concept “relationship” is proposed to represent the intraclass similarity and interclass difference, which is established by interclass couples and intraclass couples. By relationship representation and relationship learning, intraclass couples can be compressed into a compact cluster, and the distances between interclass couples are enlarged. To better discover the complex relationship hidden in change features, relationship learning is integrated into a deep learning framework, where the relationship is learned progressively. In consequence, the final change detection performance is improved with the reduced overlap between the changed class and the unchanged class. Experiments demonstrate the effectiveness of the proposed approach.