Entropy Guided Adversarial Domain Adaptation for Aerial Image Semantic Segmentation

Abstract

Recent advances on aerial image semantic segmentation mainly employ the domain adaption to transfer knowledge from the source domain to the target domain. Despite the remarkable achievement, most methods focus on the global marginal distribution alignment to reduce the domain shift between source and target domains, leading to a wrong mapping of the well-aligned features. In this article, we propose an effective unsupervised domain adaptation approach, which relies on a novel entropy guided adversarial learning algorithm, for aerial image semantic segmentation. In specific, we perform local feature alignment between domains by learning a self-adaptive weight from the target prediction probability map to measure the interdomain discrepancy. To exploit the meaningful structure information among semantic regions, we propose to utilize the graph convolutions for long-range semantic reasoning. Comprehensive experimental results on the benchmark dataset of aerial image semantic segmentation and natural scenes demonstrate the superior performance of the proposed method compared to the state-of-the-art methods.