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Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism
Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with complex cropland feature types in remote sensing image...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467744/ https://www.ncbi.nlm.nih.gov/pubmed/36105636 http://dx.doi.org/10.1155/2022/2418850 |
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author | Song, Xu Zhou, Hongyu Liu, Guoying Sheng-Xian Teo, Brian |
author_facet | Song, Xu Zhou, Hongyu Liu, Guoying Sheng-Xian Teo, Brian |
author_sort | Song, Xu |
collection | PubMed |
description | Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with complex cropland feature types in remote sensing images that resulted in blurred boundaries and low accuracy in plot partitioning. The proposed network contains different modules, such as spatial gradient guidance and dilated semantic fusion. It employs the image gradient attention guidance module to fully extract cropland plot features. This causes the feature to be transferred from the encoding layer to the decoding layer, creating layers full of key features within the cropland and making the extracted cropland information more accurate. In addition, this study also solves the problem caused by a large amount of spatial feature information, which losses easily during the downsampling process of continuous convolution in the coding layer. Aiming to solve this issue, we put forward a model for consensus fusion of multiscale spatial features to fuse each-layer feature of the coding layer through dilated convolution with different dilated ratios. This approach was proposed to make the segmentation results more comprehensive and complete. The lab findings showed that the Precision, Recall, MIoU, and F1 score of the multiscale fusion segmentation SSGNet network based on the attention mechanism had achieved 93.46%, 90.91%, 85.54%, and 92.73%, respectively. Its segmentation effect on cropland was better than other semantic segmentation networks and can effectively promote cropland semantic extraction. |
format | Online Article Text |
id | pubmed-9467744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94677442022-09-13 Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism Song, Xu Zhou, Hongyu Liu, Guoying Sheng-Xian Teo, Brian Comput Intell Neurosci Research Article Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with complex cropland feature types in remote sensing images that resulted in blurred boundaries and low accuracy in plot partitioning. The proposed network contains different modules, such as spatial gradient guidance and dilated semantic fusion. It employs the image gradient attention guidance module to fully extract cropland plot features. This causes the feature to be transferred from the encoding layer to the decoding layer, creating layers full of key features within the cropland and making the extracted cropland information more accurate. In addition, this study also solves the problem caused by a large amount of spatial feature information, which losses easily during the downsampling process of continuous convolution in the coding layer. Aiming to solve this issue, we put forward a model for consensus fusion of multiscale spatial features to fuse each-layer feature of the coding layer through dilated convolution with different dilated ratios. This approach was proposed to make the segmentation results more comprehensive and complete. The lab findings showed that the Precision, Recall, MIoU, and F1 score of the multiscale fusion segmentation SSGNet network based on the attention mechanism had achieved 93.46%, 90.91%, 85.54%, and 92.73%, respectively. Its segmentation effect on cropland was better than other semantic segmentation networks and can effectively promote cropland semantic extraction. Hindawi 2022-09-05 /pmc/articles/PMC9467744/ /pubmed/36105636 http://dx.doi.org/10.1155/2022/2418850 Text en Copyright © 2022 Xu Song et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Song, Xu Zhou, Hongyu Liu, Guoying Sheng-Xian Teo, Brian Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism |
title | Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism |
title_full | Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism |
title_fullStr | Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism |
title_full_unstemmed | Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism |
title_short | Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism |
title_sort | study of multiscale fused extraction of cropland plots in remote sensing images based on attention mechanism |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467744/ https://www.ncbi.nlm.nih.gov/pubmed/36105636 http://dx.doi.org/10.1155/2022/2418850 |
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