ADAC: an active domain adaptive network with progressive learning strategy for cloud detection of remote sensing imagery-Reference-Cited by-同舟云学术

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ADAC: an active domain adaptive network with progressive learning strategy for cloud detection of remote sensing imagery

Published:2024-08-21 Issue: Volume: Page:1-18
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Xu Kai¹^ORCID,Wenxin Wang¹^ORCID,Anling Wang¹^ORCID,Yongyi Chen¹,Xiaoyuan Deng²,Wang Taoyang³^ORCID

Affiliation:

1. School of Internet, Anhui University, Heifei, China

2. Natural Resources Monitoring Centre, Zhejiang Academy of Surveying and Mapping Science and Technology, Hangzhou, China

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

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Anhui Provincial Natural Science Foundation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2024.2389958

Reference39 articles.

1. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2. Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure

3. A theory of learning from different domains

4. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

5. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

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