期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 18, 期 11, 页码 1896-1900出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3010591
关键词
Gallium nitride; Image segmentation; Semantics; Remote sensing; Feature extraction; Adaptation models; Training; Domain adaptation; generative-adversarial network (GAN); remote sensing; semantic segmentation
类别
资金
- National Key Research and Development Program of China [2017YFC1405605]
- National Natural Science Foundation of China [61671037]
- Natural Science Foundation of Hebei Province of China [F2020202008]
- National Defense Science and Technology Key Laboratory China [61420020401]
This research proposes an unsupervised domain-adaptation method for remote sensing image segmentation, achieving joint domain adaptation through alignment of pixel and representation-level networks in an end-to-end manner, effectively addressing the multisource problem.
It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.
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