Bispace Domain Adaptation Network for Remotely Sensed Semantic Segmentation

Abstract

Supervised learning for semantic segmentation has achieved impressive success in remote sensing, while this normally has a high demand on pixel-level ground truth from the testing images (target domain). Labeling data for semantic segmentation is labor-intensive and time-consuming. To reduce the workload of manual labeling, domain adaptation (DA) utilizes preexisting labeled images from other sources (source domain) to classify the images in the target domain. In this article, we propose a bispace alignment network for DA named BSANet. BSANet is designed to have a dual-branch structure which is able to extract features in the image domain and the wavelet domain simultaneously. To minimize the discrepancy between the source and target domains, we propose a bispace adversarial learning strategy. Specifically, BSANet employs two discriminators in different spaces, one aligning the source and target feature distributions, and the other helping the classification outputs render reasonable spatial layouts. The proposed method shows the ability to train an end-to-end network for semantic segmentation without using any label in the target domain. Extensive experiments and ablation studies are conducted in cross-city scenarios. Comparative experiments with several state-of-the-art DA methods show that our method achieves the best performance.