Home > Research > Publications & Outputs > First and Second-order Information Fusion Netwo...

Electronic data

  • First_and_Second_order_Information_Fusion_Networks_for_Remote_Sensing_Scene_Classification

    Rights statement: ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 848 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

First and Second-order Information Fusion Networks for Remote Sensing Scene Classification

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • Erzhu Li
  • Alim Samat
  • Ce Zhang
  • Peijun Du
  • Wei Liu
Close
<mark>Journal publication date</mark>28/06/2021
<mark>Journal</mark>IEEE Geoscience and Remote Sensing Letters
Number of pages5
Pages (from-to)1-5
Publication StatusE-pub ahead of print
Early online date28/06/21
<mark>Original language</mark>English

Abstract

Deep convolutional networks have been the most competitive method in remote sensing scene classification. Due to the diversity and complexity of scene content, remote sensing scene classification still remains a challenging task. Recently, the second-order pooling method has attracted more interest because it can learn higher-order information and enhance the non-linear modeling ability of the networks. However, how to effectively learn second-order features and establish the discriminative feature representation of holistic images is still an open question. In this Letter, we propose a first and second-order information fusion networks (FSoI-Net) that can learn the first-order and second-order features at the same time, and construct the final feature representation by fusing the two types of features. Specifically, a self-attention-based second-order pooling (SaSoP) method based on covariance matrix is proposed to extract second-order features, and a fusion loss function is developed to jointly train the model and construct the final feature representation for the classification decision. The proposed networks have been thoroughly evaluated on three real remote sensing scene datasets and achieved better performance than the counterparts.

Bibliographic note

©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.