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Author:

Wang, Kun (Wang, Kun.) | Wang, Yingying (Wang, Yingying.) | Ding, Zhiming (Ding, Zhiming.)

Indexed by:

CPCI-S EI Scopus

Abstract:

The problem of small sample classification is to identify image categories that have not appeared in the training concentration when marking the scarce sample samples of the training data set. Such tasks are of great significance in the recognition of remote sensing scenarios. It is a problem worth studying in this field. As we all know, training a deep learning model for classification requires a considerable labeling data set, which makes the production of training data sets huge. In this article, we propose a MADB feature extraction model based on Mixed Attention Module as a base model to extract features. Using RccaEMD module as the measurement algorithm to distinguish the classification of remote sensing scenarios. In NWPU-RESISC45 dataset, AID dataset, and UC-Merced dataset, it proves that our method has achieved higher accuracy than the current advanced methods of this field.

Keyword:

few-shot learning EMD algorithm remote sensing classification

Author Community:

  • [ 1 ] [Wang, Kun]Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Yingying]Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Ding, Zhiming]Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China

Reprint Author's Address:

  • [Ding, Zhiming]Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China;;

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Source :

SPATIAL DATA AND INTELLIGENCE, SPATIALDI 2024

ISSN: 0302-9743

Year: 2024

Volume: 14619

Page: 255-273

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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