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Classification des séries temporelles de données satellitaires à l'aide du mécanisme d'attention auto-temporel

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, School of Electronic, Electrical and Communication Engineering, Beijing 100049, China

  • Email：zhangweixiong@aircas.ac.cn
  • Introduction：张伟雄，研究方向为遥感图像处理。E-mail：zhangweixiong@aircas.ac.cn
ZHANG Weixiong12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

TANG Ping1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：zhangzheng@aircas.ac.cn
  • Introduction：张正，研究方向为遥感图像处理，遥感时间序列分析。E-mail：zhangzheng@aircas.ac.cn
ZHANG Zheng1*

résumé

La série temporelle d'images satellitaires fournit une base de données importante pour l'étude de la classification de la couverture terrestre. L'extraction des caractéristiques de classification temporelle à l'aide de l'apprentissage en profondeur a toujours été un sujet de recherche brûlant, tandis que les modèles d'apprentissage profond basés sur les réseaux récurrents et convolutionnels ont souvent du mal à obtenir des résultats de classification de grande précision sur de petits échantillons d'entraînement. Pour résoudre ce problème, cet article introduit les dernières méthodes de traitement du langage naturel dans le domaine de l'attention automatique pour la classification des séries temporelles multispectrales satellitaires. En améliorant l'encodeur Transformer : (1) ajout d'une couche d'élévation des caractéristiques avant l'attention multi-têtes, pour améliorer l'information spectrale; (2) utilisation de l'étirement après la réduction de dimension au lieu du maximum global poolin

mots-clés

Mécanisme d'attention ; Apprentissage profond ; Séries temporelles de données satellitaires ; Classification de la couverture terrestre ; Échantillons déséquilibrés

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