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...
Autores principales: | , , , |
---|---|
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 |