Remote Sensing Image Scene Classification Based on Global Self-Attention Module

The complexity of scene images makes the research on remote-sensing image scene classification challenging. With the wide application of deep learning in recent years, many remote-sensing scene classification methods using a convolutional neural network (CNN) have emerged. Current CNN usually output...

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Autores principales:	Qingwen Li, Dongmei Yan, Wanrong Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	remote-sensing image scene classification convolutional neural network (CNN) global self-attention module Science Q
Acceso en línea:	https://doaj.org/article/4d717f820e474328b343a656b62df52c
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id	oai:doaj.org-article:4d717f820e474328b343a656b62df52c
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spelling	oai:doaj.org-article:4d717f820e474328b343a656b62df52c2021-11-25T18:54:06ZRemote Sensing Image Scene Classification Based on Global Self-Attention Module10.3390/rs132245422072-4292https://doaj.org/article/4d717f820e474328b343a656b62df52c2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4542https://doaj.org/toc/2072-4292The complexity of scene images makes the research on remote-sensing image scene classification challenging. With the wide application of deep learning in recent years, many remote-sensing scene classification methods using a convolutional neural network (CNN) have emerged. Current CNN usually output global information by integrating the depth features extricated from the convolutional layer through the fully connected layer; however, the global information extracted is not comprehensive. This paper proposes an improved remote-sensing image scene classification method based on a global self-attention module to address this problem. The global information is derived from the depth characteristics extracted by the CNN. In order to better express the semantic information of the remote-sensing image, the multi-head self-attention module is introduced for global information augmentation. Meanwhile, the local perception unit is utilized to improve the self-attention module’s representation capabilities for local objects. The proposed method’s effectiveness is validated through comparative experiments with various training ratios and different scales on public datasets (UC Merced, AID, and NWPU-NESISC45). The precision of our proposed model is significantly improved compared to other methods for remote-sensing image scene classification.Qingwen LiDongmei YanWanrong WuMDPI AGarticleremote-sensing imagescene classificationconvolutional neural network (CNN)global self-attention moduleScienceQENRemote Sensing, Vol 13, Iss 4542, p 4542 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote-sensing image scene classification convolutional neural network (CNN) global self-attention module Science Q
spellingShingle	remote-sensing image scene classification convolutional neural network (CNN) global self-attention module Science Q Qingwen Li Dongmei Yan Wanrong Wu Remote Sensing Image Scene Classification Based on Global Self-Attention Module
description	The complexity of scene images makes the research on remote-sensing image scene classification challenging. With the wide application of deep learning in recent years, many remote-sensing scene classification methods using a convolutional neural network (CNN) have emerged. Current CNN usually output global information by integrating the depth features extricated from the convolutional layer through the fully connected layer; however, the global information extracted is not comprehensive. This paper proposes an improved remote-sensing image scene classification method based on a global self-attention module to address this problem. The global information is derived from the depth characteristics extracted by the CNN. In order to better express the semantic information of the remote-sensing image, the multi-head self-attention module is introduced for global information augmentation. Meanwhile, the local perception unit is utilized to improve the self-attention module’s representation capabilities for local objects. The proposed method’s effectiveness is validated through comparative experiments with various training ratios and different scales on public datasets (UC Merced, AID, and NWPU-NESISC45). The precision of our proposed model is significantly improved compared to other methods for remote-sensing image scene classification.
format	article
author	Qingwen Li Dongmei Yan Wanrong Wu
author_facet	Qingwen Li Dongmei Yan Wanrong Wu
author_sort	Qingwen Li
title	Remote Sensing Image Scene Classification Based on Global Self-Attention Module
title_short	Remote Sensing Image Scene Classification Based on Global Self-Attention Module
title_full	Remote Sensing Image Scene Classification Based on Global Self-Attention Module
title_fullStr	Remote Sensing Image Scene Classification Based on Global Self-Attention Module
title_full_unstemmed	Remote Sensing Image Scene Classification Based on Global Self-Attention Module
title_sort	remote sensing image scene classification based on global self-attention module
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4d717f820e474328b343a656b62df52c
work_keys_str_mv	AT qingwenli remotesensingimagesceneclassificationbasedonglobalselfattentionmodule AT dongmeiyan remotesensingimagesceneclassificationbasedonglobalselfattentionmodule AT wanrongwu remotesensingimagesceneclassificationbasedonglobalselfattentionmodule
_version_	1718410584999854080

Remote Sensing Image Scene Classification Based on Global Self-Attention Module

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