A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification

Yuanzhen Shuai; Qi Ao Yuan; Shanshan Zhao

doi:10.1109/IGARSS46834.2022.9884780

A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification

Yuanzhen Shuai, Qi Ao Yuan, Shanshan Zhao

School of AI and Advanced Computing

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

This paper proposes a novel model for remote sensing image classification based on CBAM-CNN (Convolutional Block Attention Module-Convolutional Neural Network). CBAM-CNN is a well-known and effective model. However, it is limited when it comes to high-level features due to its shared spatial attention mechanism and narrow sampling range in squeeze-and-excitation module (SEM). We disentangle the shared attention to channel-independent spatial attention and expand the sampling range of SEM. The results show that the proposed approach outperforms other CNN-based models on two large-scale benchmark datasets.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3628-3631
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9884780
Publication status	Published - 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Keywords

Attention
Convolution Neural Networks
Image Classification
Remote Sensing
Scene Classification

Access to Document

10.1109/IGARSS46834.2022.9884780

Cite this

Shuai, Y., Yuan, Q. A., & Zhao, S. (2022). A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 3628-3631). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9884780

Shuai, Yuanzhen ; Yuan, Qi Ao ; Zhao, Shanshan. / A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3628-3631 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{4b5459c577234ffd958552ab568dbe53,

title = "A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification",

abstract = "This paper proposes a novel model for remote sensing image classification based on CBAM-CNN (Convolutional Block Attention Module-Convolutional Neural Network). CBAM-CNN is a well-known and effective model. However, it is limited when it comes to high-level features due to its shared spatial attention mechanism and narrow sampling range in squeeze-and-excitation module (SEM). We disentangle the shared attention to channel-independent spatial attention and expand the sampling range of SEM. The results show that the proposed approach outperforms other CNN-based models on two large-scale benchmark datasets.",

keywords = "Attention, Convolution Neural Networks, Image Classification, Remote Sensing, Scene Classification",

author = "Yuanzhen Shuai and Yuan, {Qi Ao} and Shanshan Zhao",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.1109/IGARSS46834.2022.9884780",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3628--3631",

booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

}

Shuai, Y, Yuan, QA & Zhao, S 2022, A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 3628-3631, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9884780

A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification. / Shuai, Yuanzhen; Yuan, Qi Ao; Zhao, Shanshan.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3628-3631 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification

AU - Shuai, Yuanzhen

AU - Yuan, Qi Ao

AU - Zhao, Shanshan

PY - 2022

Y1 - 2022

N2 - This paper proposes a novel model for remote sensing image classification based on CBAM-CNN (Convolutional Block Attention Module-Convolutional Neural Network). CBAM-CNN is a well-known and effective model. However, it is limited when it comes to high-level features due to its shared spatial attention mechanism and narrow sampling range in squeeze-and-excitation module (SEM). We disentangle the shared attention to channel-independent spatial attention and expand the sampling range of SEM. The results show that the proposed approach outperforms other CNN-based models on two large-scale benchmark datasets.

AB - This paper proposes a novel model for remote sensing image classification based on CBAM-CNN (Convolutional Block Attention Module-Convolutional Neural Network). CBAM-CNN is a well-known and effective model. However, it is limited when it comes to high-level features due to its shared spatial attention mechanism and narrow sampling range in squeeze-and-excitation module (SEM). We disentangle the shared attention to channel-independent spatial attention and expand the sampling range of SEM. The results show that the proposed approach outperforms other CNN-based models on two large-scale benchmark datasets.

KW - Attention

KW - Convolution Neural Networks

KW - Image Classification

KW - Remote Sensing

KW - Scene Classification

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U2 - 10.1109/IGARSS46834.2022.9884780

DO - 10.1109/IGARSS46834.2022.9884780

M3 - Conference Proceeding

AN - SCOPUS:85141896619

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 3628

EP - 3631

BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022

Y2 - 17 July 2022 through 22 July 2022

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Shuai Y, Yuan QA, Zhao S. A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3628-3631. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9884780

A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification

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Keywords

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