Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images

Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale bitemporal images that contain building change, due to both its rarity and sparsity. Contemporary methods to tackle the data insufficiency mainly focus on transformation-based global image augmentation and cost-sensitive algorithms. In this article, we propose a novel data-level solution, namely, Instance-level change Augmentation (IAug), to generate bitemporal images that contain changes involving plenty and diverse buildings by leveraging generative adversarial training. The key of IAug is to blend synthesized building instances onto appropriate positions of one of the bitemporal images. To achieve this, a building generator is employed to produce realistic building images that are consistent with the given layouts. Diverse styles are later transferred onto the generated images. We further propose context-aware blending for a realistic composite of the building and the background. We augment the existing CD data sets and also design a simple yet effective CD model—CD network (CDNet). Our method (CDNet + IAug) has achieved state-of-the-art results in two building CD data sets (LEVIR-CD and WHU-CD). Interestingly, we achieve comparable results with only 20% of the training data as the current state-of-the-art methods using 100% data. Extensive experiments have validated the effectiveness of the proposed IAug. Our augmented data set has a lower risk of class imbalance than the original one. Conventional learning on the synthesized data set outperforms several popular cost-sensitive algorithms on the original data set. Our code and data are available at https://github.com/justchenhao/IAug_CDNet.