Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network
Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectra...
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ftdoajarticles:oai:doaj.org/article:e84449498d204abaa2d154e9d6379079 2023-05-15T18:16:15+02:00 Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network Yanling Han Pengxia Cui Yun Zhang Ruyan Zhou Shuhu Yang Jing Wang 2021-01-01T00:00:00Z https://doi.org/10.1155/2021/9928351 https://doaj.org/article/e84449498d204abaa2d154e9d6379079 EN eng Hindawi Limited http://dx.doi.org/10.1155/2021/9928351 https://doaj.org/toc/1024-123X https://doaj.org/toc/1563-5147 1024-123X 1563-5147 doi:10.1155/2021/9928351 https://doaj.org/article/e84449498d204abaa2d154e9d6379079 Mathematical Problems in Engineering, Vol 2021 (2021) Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 article 2021 ftdoajarticles https://doi.org/10.1155/2021/9928351 2022-12-31T03:50:32Z Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Mathematical Problems in Engineering 2021 1 10 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 |
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Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Yanling Han Pengxia Cui Yun Zhang Ruyan Zhou Shuhu Yang Jing Wang Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network |
topic_facet |
Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 |
description |
Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance. |
format |
Article in Journal/Newspaper |
author |
Yanling Han Pengxia Cui Yun Zhang Ruyan Zhou Shuhu Yang Jing Wang |
author_facet |
Yanling Han Pengxia Cui Yun Zhang Ruyan Zhou Shuhu Yang Jing Wang |
author_sort |
Yanling Han |
title |
Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network |
title_short |
Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network |
title_full |
Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network |
title_fullStr |
Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network |
title_full_unstemmed |
Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network |
title_sort |
remote sensing sea ice image classification based on multilevel feature fusion and residual network |
publisher |
Hindawi Limited |
publishDate |
2021 |
url |
https://doi.org/10.1155/2021/9928351 https://doaj.org/article/e84449498d204abaa2d154e9d6379079 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Mathematical Problems in Engineering, Vol 2021 (2021) |
op_relation |
http://dx.doi.org/10.1155/2021/9928351 https://doaj.org/toc/1024-123X https://doaj.org/toc/1563-5147 1024-123X 1563-5147 doi:10.1155/2021/9928351 https://doaj.org/article/e84449498d204abaa2d154e9d6379079 |
op_doi |
https://doi.org/10.1155/2021/9928351 |
container_title |
Mathematical Problems in Engineering |
container_volume |
2021 |
container_start_page |
1 |
op_container_end_page |
10 |
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1766189757029679104 |