Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data
Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detectio...
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ftdoajarticles:oai:doaj.org/article:6bde7adbac534dc9be86445c98a62e12 2023-05-15T15:35:04+02:00 Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data Yanling Han Jue Li Yun Zhang Zhonghua Hong Jing Wang 2017-05-01T00:00:00Z https://doi.org/10.3390/s17051124 https://doaj.org/article/6bde7adbac534dc9be86445c98a62e12 EN eng MDPI AG http://www.mdpi.com/1424-8220/17/5/1124 https://doaj.org/toc/1424-8220 1424-8220 doi:10.3390/s17051124 https://doaj.org/article/6bde7adbac534dc9be86445c98a62e12 Sensors, Vol 17, Iss 5, p 1124 (2017) sea ice similarity measure band selection classification hyperspectral image Chemical technology TP1-1185 article 2017 ftdoajarticles https://doi.org/10.3390/s17051124 2022-12-30T21:55:20Z Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. Article in Journal/Newspaper Baffin Bay Baffin Bay Baffin Sea ice Directory of Open Access Journals: DOAJ Articles Baffin Bay Sensors 17 5 1124 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
sea ice similarity measure band selection classification hyperspectral image Chemical technology TP1-1185 |
spellingShingle |
sea ice similarity measure band selection classification hyperspectral image Chemical technology TP1-1185 Yanling Han Jue Li Yun Zhang Zhonghua Hong Jing Wang Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data |
topic_facet |
sea ice similarity measure band selection classification hyperspectral image Chemical technology TP1-1185 |
description |
Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. |
format |
Article in Journal/Newspaper |
author |
Yanling Han Jue Li Yun Zhang Zhonghua Hong Jing Wang |
author_facet |
Yanling Han Jue Li Yun Zhang Zhonghua Hong Jing Wang |
author_sort |
Yanling Han |
title |
Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data |
title_short |
Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data |
title_full |
Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data |
title_fullStr |
Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data |
title_full_unstemmed |
Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data |
title_sort |
sea ice detection based on an improved similarity measurement method using hyperspectral data |
publisher |
MDPI AG |
publishDate |
2017 |
url |
https://doi.org/10.3390/s17051124 https://doaj.org/article/6bde7adbac534dc9be86445c98a62e12 |
geographic |
Baffin Bay |
geographic_facet |
Baffin Bay |
genre |
Baffin Bay Baffin Bay Baffin Sea ice |
genre_facet |
Baffin Bay Baffin Bay Baffin Sea ice |
op_source |
Sensors, Vol 17, Iss 5, p 1124 (2017) |
op_relation |
http://www.mdpi.com/1424-8220/17/5/1124 https://doaj.org/toc/1424-8220 1424-8220 doi:10.3390/s17051124 https://doaj.org/article/6bde7adbac534dc9be86445c98a62e12 |
op_doi |
https://doi.org/10.3390/s17051124 |
container_title |
Sensors |
container_volume |
17 |
container_issue |
5 |
container_start_page |
1124 |
_version_ |
1766365372479438848 |