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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ftmdpi:oai:mdpi.com:/1424-8220/17/5/1124/ 2023-08-20T04:05:23+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 2017-05-15 application/pdf https://doi.org/10.3390/s17051124 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/s17051124 https://creativecommons.org/licenses/by/4.0/ Sensors; Volume 17; Issue 5; Pages: 1124 sea ice similarity measure band selection classification hyperspectral image Text 2017 ftmdpi https://doi.org/10.3390/s17051124 2023-07-31T21:07:07Z 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. Text Baffin Bay Baffin Bay Baffin Sea ice MDPI Open Access Publishing Baffin Bay Sensors 17 5 1124 |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
sea ice similarity measure band selection classification hyperspectral image |
spellingShingle |
sea ice similarity measure band selection classification hyperspectral image 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 |
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 |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2017 |
url |
https://doi.org/10.3390/s17051124 |
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; Volume 17; Issue 5; Pages: 1124 |
op_relation |
https://dx.doi.org/10.3390/s17051124 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/s17051124 |
container_title |
Sensors |
container_volume |
17 |
container_issue |
5 |
container_start_page |
1124 |
_version_ |
1774715905220018176 |