BiFDANet: Unsupervised Bidirectional Domain Adaptation for Semantic Segmentation of Remote Sensing Images

When segmenting massive amounts of remote sensing images collected from different satellites or geographic locations (cities), the pre-trained deep learning models cannot always output satisfactory predictions. To deal with this issue, domain adaptation has been widely utilized to enhance the genera...

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Detaylı Bibliyografya
Asıl Yazarlar:	Yuxiang Cai, Yingchun Yang, Qiyi Zheng, Zhengwei Shen, Yongheng Shang, Jianwei Yin, Zhongtian Shi
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	MDPI AG 2022-01-01
Seri Bilgileri:	Remote Sensing
Konular:	unsupervised domain adaptation bidirectional domain adaptation convolutional neural networks (CNNs) image-to-image translation generative adversarial networks (GANs) remote sensing images
Online Erişim:	https://www.mdpi.com/2072-4292/14/1/190
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BiFDANet: Unsupervised Bidirectional Domain Adaptation for Semantic Segmentation of Remote Sensing Images

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