SPGAN-DA: Semantic-Preserved Generative Adversarial Network for Domain Adaptive Remote Sensing Image Semantic Segmentation

Unsupervised domain adaptation for remote sensing semantic segmentation seeks to adapt a model trained on the labeled source domain to the unlabeled target domain. One of the most promising ways is to translate images from the source domain to the target domain to align the spectral information or imaging mode by the generative adversarial network (GAN). However, source-to-target translation often brings bias in the translated images causing limited performance, as semantic information is not well considered in the translation procedure. To overcome this limitation, we present an innovative semantic-preserved generative adversarial network (SPGAN), designed to mitigate the image translation bias and then leverage the translated images as well as unlabeled target images by class distribution alignment (CDA) module to train a domain adaptive semantic segmentation model. The above two stages are coupled together to form a unified framework called SPGAN-DA. Specifically, we first conduct semantic invariant translation from source to target domain, which is achieved by introducing representation-invariant and semantic-preserved constraints to the GAN model. To further narrow the landscape layout gap between the translated and target images, CDA semantic segmentation is proposed. CDA semantic segmentation consists of two aspects. At the model input level, object discrepancy is eliminated by introducing the ClassMix operation. At the model output level, boundary enhancement is proposed to refine the performance of object boundaries. Extensive experiments on three typical remote sensing cross-domain semantic segmentation benchmarks demonstrate the effectiveness and generality of our proposed method, which competes favorably against existing state-of-the-art methods.