Evaluating generative adversarial networks based image-level domain transfer for multi-source remote sensing image segmentation and object detection-Reference-Cited by-同舟云学术

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Evaluating generative adversarial networks based image-level domain transfer for multi-source remote sensing image segmentation and object detection

Published:2020-07-07 Issue:19 Volume:41 Page:7343-7367
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Li Xue¹,Luo Muying²,Ji Shunping²,Zhang Li¹,Lu Meng³

Affiliation:

1. Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, China

2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

3. Faculty of Geoscience, Department of Physical Geography, Utrecht University, Utrecht, The Netherlands

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1757782

Reference44 articles.

1. Arjovsky, M., S. Chintala, and L. Bottou. 2017. “Wasserstein GAN.” arXiv Preprint arXiv:1701.07875.

2. Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

3. Learning a two-stage CNN model for multi-sized building detection in remote sensing images

4. AutoAugment: Learning Augmentation Strategies From Data

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