Dual Network with Cumulative Learning for Change Detection

Abstract

VHR(Very High Resolution) image change detection is a hot topic in remote sensing. With the development of deep learning, change detection performances have been improved significantly. However, the data imbalance between the unchanged class and the changed class as well as the data imbalance between different change types greatly impacts the network training process and the final performance. To address this problem, a novel method is proposed in this paper based on dual network structure and cumulative learning strategy. With the help of dual network structure, the deep learning network is more robust in balancing unchanged class and changed class. By cumulative learning, the network training procedure is more stable. Extensive experiments demonstrate the effectiveness of the proposed method on a variety of change detection datasets and existing change detection frameworks.