предыдущий|следующий

Wetland classification based on Sentinel-2 and 3D multisource domain self-attention model

  • role： First author第一作者
  • Affiliation：

    School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China

  • Email：louanj@mail2.sysu.edu.cn
  • Introduction：楼桉君，研究方向为湿地遥感。E-mail： louanj@mail2.sysu.edu.cn
LOU Anjun1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China

    Guangdong Provincial Laboratory of Marine Science and Engineering (Zhuhai), Zhuhai 519082, China

  • Email：Hezh8@mail.sysu.edu.cn
  • Introduction：贺智，研究方向为高分/高光谱遥感、模式识别和深度学习等。E-mail： Hezh8@mail.sysu.edu.cn
HE Zhi12*,  
  • Affiliation：

    School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China

XIAO Man1,  
  • Affiliation：

    School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China

LI Xinyuan1

реферат

Accurate wetland classification methods can quickly grasp the spatial-temporal variation characteristics of wetlands and play an important role in wetland research. Considering the limitation of the existing wetland classification method based on few-shot learning to the use of target or single-source domain dataset, this paper proposes a 3D multisource domain self-attention few-shot learning (3D-MDAFSL) model. First, combining the advantages of convolution and attention mechanism, a 3D feature extractor based on self-attention mechanism and deep residual convolution is designed. Then, the conditional adversarial domain adaptation strategy is used to achieve multisource domain distribution alignment, and few-shot learning is performed separately in each domain. Finally, the features extracted by the trained model are imputed to the K-nearest neighbor classifier to obtain classification results. Results show that compared with the framework without feature extraction, the 3D feature extractor improves the overall accuracy by approximately 6.79%. When using multisource domain datasets, the overall accuracy of the 3D-MDAFSL model for the Sentinel-2 wetland dataset in Zhongshan City can reach 93.52%, which is a significant improvement compared with the existing algorithms. The 3D-MDAFSL model proposed in this paper has good application value in the high-precision extraction and classification of wetland features.

ключеви́че слова́

remote sensing;Few-shot;wetland classification;Multi-source domain adaption;Self-attention

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