Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features
Mapping different types of sea ice that form, grow, and melt in polar oceans is essential for shipping navigation, climate change modeling, and local community safety. Currently, ice charts are manually generated by analysts at the Canadian Ice Service based on dual-polarized RADARSAT-2/RADARSAT Con...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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ftdoajarticles:oai:doaj.org/article:0319da2a2c4d4bae9fe2d0437f584553 2023-05-15T15:40:37+02:00 Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features Mingzhe Jiang David A. Clausi Linlin Xu 2022-01-01T00:00:00Z https://doi.org/10.1109/JSTARS.2022.3205849 https://doaj.org/article/0319da2a2c4d4bae9fe2d0437f584553 EN eng IEEE https://ieeexplore.ieee.org/document/9887856/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2022.3205849 https://doaj.org/article/0319da2a2c4d4bae9fe2d0437f584553 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 7964-7977 (2022) Classification RADARSAT-2 random forest (RF) sea ice segmentation synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 article 2022 ftdoajarticles https://doi.org/10.1109/JSTARS.2022.3205849 2022-12-30T19:57:37Z Mapping different types of sea ice that form, grow, and melt in polar oceans is essential for shipping navigation, climate change modeling, and local community safety. Currently, ice charts are manually generated by analysts at the Canadian Ice Service based on dual-polarized RADARSAT-2/RADARSAT Constellation Mission imagery on a daily basis. Inspired by the demand for a computer-based mapping system, we have developed an automatic sea-ice classification method that integrates spatial contexture (unsupervised segmentation) with textural features (supervised pixel-level labeling). First, the full-scene image is oversegmented, and the segments are merged into homogeneous regions across the entire scene. Second, pixel-based classifiers (support vector machine and random forest) are compared for their ability to label the generated homogeneous regions. Finally, the segmentation and labeling are combined using a proposed energy function. The proposed method was tested on 18 dual-polarization RADARSAT-2 scenes acquired over the Beaufort Sea. This dataset contains water, young ice, first-year ice, and multiyear ice covering melt, summer, and freeze-up seasons. The proposed method obtains an average classification accuracy of 86.33% based on the leave-one-out validation. The experimental results show that the proposed method achieves promising classification results in both the quantity and quality measurements compared with benchmark methods. The robustness against incidence angle variance indicates that the proposed method is well qualified for operational sea-ice mapping. Article in Journal/Newspaper Beaufort Sea Sea ice Directory of Open Access Journals: DOAJ Articles IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 7964 7977 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Classification RADARSAT-2 random forest (RF) sea ice segmentation synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Classification RADARSAT-2 random forest (RF) sea ice segmentation synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Mingzhe Jiang David A. Clausi Linlin Xu Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features |
topic_facet |
Classification RADARSAT-2 random forest (RF) sea ice segmentation synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
description |
Mapping different types of sea ice that form, grow, and melt in polar oceans is essential for shipping navigation, climate change modeling, and local community safety. Currently, ice charts are manually generated by analysts at the Canadian Ice Service based on dual-polarized RADARSAT-2/RADARSAT Constellation Mission imagery on a daily basis. Inspired by the demand for a computer-based mapping system, we have developed an automatic sea-ice classification method that integrates spatial contexture (unsupervised segmentation) with textural features (supervised pixel-level labeling). First, the full-scene image is oversegmented, and the segments are merged into homogeneous regions across the entire scene. Second, pixel-based classifiers (support vector machine and random forest) are compared for their ability to label the generated homogeneous regions. Finally, the segmentation and labeling are combined using a proposed energy function. The proposed method was tested on 18 dual-polarization RADARSAT-2 scenes acquired over the Beaufort Sea. This dataset contains water, young ice, first-year ice, and multiyear ice covering melt, summer, and freeze-up seasons. The proposed method obtains an average classification accuracy of 86.33% based on the leave-one-out validation. The experimental results show that the proposed method achieves promising classification results in both the quantity and quality measurements compared with benchmark methods. The robustness against incidence angle variance indicates that the proposed method is well qualified for operational sea-ice mapping. |
format |
Article in Journal/Newspaper |
author |
Mingzhe Jiang David A. Clausi Linlin Xu |
author_facet |
Mingzhe Jiang David A. Clausi Linlin Xu |
author_sort |
Mingzhe Jiang |
title |
Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features |
title_short |
Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features |
title_full |
Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features |
title_fullStr |
Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features |
title_full_unstemmed |
Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features |
title_sort |
sea-ice mapping of radarsat-2 imagery by integrating spatial contexture with textural features |
publisher |
IEEE |
publishDate |
2022 |
url |
https://doi.org/10.1109/JSTARS.2022.3205849 https://doaj.org/article/0319da2a2c4d4bae9fe2d0437f584553 |
genre |
Beaufort Sea Sea ice |
genre_facet |
Beaufort Sea Sea ice |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 7964-7977 (2022) |
op_relation |
https://ieeexplore.ieee.org/document/9887856/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2022.3205849 https://doaj.org/article/0319da2a2c4d4bae9fe2d0437f584553 |
op_doi |
https://doi.org/10.1109/JSTARS.2022.3205849 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
15 |
container_start_page |
7964 |
op_container_end_page |
7977 |
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1766373241134252032 |