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

Research

Associated organisational unit

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:

Keywords

View graph of relations

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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

First and Second-order Information Fusion Networks for Remote Sensing Scene Classification. / Li, Erzhu; Samat, Alim; Zhang, Ce et al.
In: IEEE Geoscience and Remote Sensing Letters, Vol. 19, 8009406, 01.01.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Li, E, Samat, A, Zhang, C, Du, P & Liu, W 2022, 'First and Second-order Information Fusion Networks for Remote Sensing Scene Classification', IEEE Geoscience and Remote Sensing Letters, vol. 19, 8009406. https://doi.org/10.1109/LGRS.2021.3090045

APA

Li, E., Samat, A., Zhang, C., Du, P., & Liu, W. (2022). First and Second-order Information Fusion Networks for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 19, Article 8009406. https://doi.org/10.1109/LGRS.2021.3090045

Vancouver

Li E, Samat A, Zhang C, Du P, Liu W. First and Second-order Information Fusion Networks for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters. 2022 Jan 1;19:8009406. Epub 2021 Jun 28. doi: 10.1109/LGRS.2021.3090045

Author

Li, Erzhu ; Samat, Alim ; Zhang, Ce et al. / First and Second-order Information Fusion Networks for Remote Sensing Scene Classification. In: IEEE Geoscience and Remote Sensing Letters. 2022 ; Vol. 19.

Bibtex

@article{16881945a46642808431be32888f80e5,
title = "First and Second-order Information Fusion Networks for Remote Sensing Scene Classification",
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.",
keywords = "Deep learning, second-order pooling, self-attention mechanism, information fusion, scene classification",
author = "Erzhu Li and Alim Samat and Ce Zhang and Peijun Du and Wei Liu",
note = "{\textcopyright}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. ",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/LGRS.2021.3090045",
language = "English",
volume = "19",
journal = "IEEE Geoscience and Remote Sensing Letters",
issn = "1545-598X",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",

}

RIS

TY - JOUR

T1 - First and Second-order Information Fusion Networks for Remote Sensing Scene Classification

AU - Li, Erzhu

AU - Samat, Alim

AU - Zhang, Ce

AU - Du, Peijun

AU - Liu, Wei

N1 - ©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.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - 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.

AB - 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.

KW - Deep learning

KW - second-order pooling

KW - self-attention mechanism

KW - information fusion

KW - scene classification

U2 - 10.1109/LGRS.2021.3090045

DO - 10.1109/LGRS.2021.3090045

M3 - Journal article

VL - 19

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

SN - 1545-598X

M1 - 8009406

ER -