Cargando…

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

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, categ...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Yuan, Ma, Hongbing, Alifu, Kuerban, Lv, Yalong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862669/
https://www.ncbi.nlm.nih.gov/pubmed/33542421
http://dx.doi.org/10.1038/s41598-021-82704-4
_version_ 1783647337738928128
author Wang, Yuan
Ma, Hongbing
Alifu, Kuerban
Lv, Yalong
author_facet Wang, Yuan
Ma, Hongbing
Alifu, Kuerban
Lv, Yalong
author_sort Wang, Yuan
collection PubMed
description 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 Online
Article
Text
id pubmed-7862669
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-78626692021-02-08 Remote sensing image description based on word embedding and end-to-end deep learning Wang, Yuan Ma, Hongbing Alifu, Kuerban Lv, Yalong Sci Rep Article 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. Nature Publishing Group UK 2021-02-04 /pmc/articles/PMC7862669/ /pubmed/33542421 http://dx.doi.org/10.1038/s41598-021-82704-4 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Wang, Yuan
Ma, Hongbing
Alifu, Kuerban
Lv, Yalong
Remote sensing image description based on word embedding and end-to-end deep learning
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862669/
https://www.ncbi.nlm.nih.gov/pubmed/33542421
http://dx.doi.org/10.1038/s41598-021-82704-4
work_keys_str_mv AT wangyuan remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning
AT mahongbing remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning
AT alifukuerban remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning
AT lvyalong remotesensingimagedescriptionbasedonwordembeddingandendtoenddeeplearning