Remote sensing image description based on word embedding and end-to-end deep learning

Abstract This study proposes an end-to-end image description generation model based on word embedding technology to realise the classification and identification of Populus euphratica and Tamarix in complex remote sensing images by providing descriptions in precise and concise natural sentences. Fir...

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Autores principales:	Yuan Wang, Hongbing Ma, Kuerban Alifu, Yalong Lv
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a4f7d8083d4d486fa8fce75c5f6fc3bf
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spelling	oai:doaj.org-article:a4f7d8083d4d486fa8fce75c5f6fc3bf2021-12-02T14:06:57ZRemote sensing image description based on word embedding and end-to-end deep learning10.1038/s41598-021-82704-42045-2322https://doaj.org/article/a4f7d8083d4d486fa8fce75c5f6fc3bf2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82704-4https://doaj.org/toc/2045-2322Abstract This study proposes an end-to-end image description generation model based on word embedding technology to realise the classification and identification of Populus euphratica and Tamarix in complex remote sensing images by providing descriptions in precise and concise natural sentences. First, category ambiguity over large-scale regions in remote sensing images is addressed by introducing the co-occurrence matrix and global vectors for word representation to generate the word vector features of the object to be identified. Second, a new multi-level end-to-end model is employed to further describe the content of remote sensing images and to better advance the description tasks for P. euphratica and Tamarix in remote sensing images. Experimental results reveal that the natural language sentences generated using this method can better describe P. euphratica and Tamarix in remote sensing images compared with conventional deep learning methods.Yuan WangHongbing MaKuerban AlifuYalong LvNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuan Wang Hongbing Ma Kuerban Alifu Yalong Lv Remote sensing image description based on word embedding and end-to-end deep learning
description	Abstract This study proposes an end-to-end image description generation model based on word embedding technology to realise the classification and identification of Populus euphratica and Tamarix in complex remote sensing images by providing descriptions in precise and concise natural sentences. First, category ambiguity over large-scale regions in remote sensing images is addressed by introducing the co-occurrence matrix and global vectors for word representation to generate the word vector features of the object to be identified. Second, a new multi-level end-to-end model is employed to further describe the content of remote sensing images and to better advance the description tasks for P. euphratica and Tamarix in remote sensing images. Experimental results reveal that the natural language sentences generated using this method can better describe P. euphratica and Tamarix in remote sensing images compared with conventional deep learning methods.
format	article
author	Yuan Wang Hongbing Ma Kuerban Alifu Yalong Lv
author_facet	Yuan Wang Hongbing Ma Kuerban Alifu Yalong Lv
author_sort	Yuan Wang
title	Remote sensing image description based on word embedding and end-to-end deep learning
title_short	Remote sensing image description based on word embedding and end-to-end deep learning
title_full	Remote sensing image description based on word embedding and end-to-end deep learning
title_fullStr	Remote sensing image description based on word embedding and end-to-end deep learning
title_full_unstemmed	Remote sensing image description based on word embedding and end-to-end deep learning
title_sort	remote sensing image description based on word embedding and end-to-end deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a4f7d8083d4d486fa8fce75c5f6fc3bf
work_keys_str_mv	AT yuanwang remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning AT hongbingma remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning AT kuerbanalifu remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning AT yalonglv remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning
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Remote sensing image description based on word embedding and end-to-end deep learning

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