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朱婷婷--Antarctic Sea-Ice Classification Based on Conditional Random Fields From RADARSAT-2 Dual-Polarization Satellite Images

发布日期:2016-11-30 16:21:45 阅读次数:[1654]次 作者:

核心提示:来源出版物: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

作者: Zhu, TT (Zhu, Tingting); Li, F (Li, Fei); Heygster, G (Heygster, Georg); Zhang, SK (Zhang, Shengkai)
来源出版物: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING  卷: 9  期: 6  特刊: SI  页: 2451-2467  DOI: 10.1109/JSTARS.2016.2551318  出版年: JUN 2016
摘要: In January 2014, Chinese National Antarctic Research Expedition (CHINARE) 30th cruise raised public concern since the Xuelong, the Chinese polar research vessel, was trapped in the sea-ice zone (66 degrees 39'20.88 '' S, 144 degrees 25'2.28 '' E) in the vicinity of the Adelie Depression area on the east Antarctic continent. This event highlighted the importance of an operational sea-ice classification map for ice routing to serve ship navigation. In this paper, unprecedented Antarctic sea-ice classification algorithms from RADARSAT-2 satellite dual-polarization synthetic aperture radar (SAR) images were developed using the conditional random fields (CRF) approach by including multiple features from sea-ice concentration, gray-level cooccurrence matrix textures, polarization ratio, backscatter coefficients, and intensity data. Coincident RADARSAT-2 Satellite SAR datasets with five scenes were collected for ice classification into categories such as open water, thin ice, smooth first year ice, deformed first year ice, and old ice during the CHINARE-30th cruise. The effects of deformation, rafting, and ridging during the spring-summer transition period were overwhelmed by the spatial and contextual CRF models in combination with the rich features extracted for sea-ice classification. Four strategies including statistical distribution and region connection, multiple features and support vector machine (SVM) integrated into the CRF model are proposed to describe the sea-ice-type relationships among pixels. By conducting comparative experiments between the proposed methods and state-of-the-art sea-ice classification based on the SVM algorithm, the best was obtained from the SVM-based CRF (SVM-CRF) algorithm for sea-ice classification with respect to the three scenes from the Indian Ocean sector and two scenes of Pacific Ocean sectors including medium-resolution dual-polarization SAR imagery with a pixel spacing of 50 m and higher resolution dual-polarization SAR imagery with a pixel spacing of 6.25 m. Results indicate that the SVM-CRF approach has the capacity for improving sea-ice classification, which can provide accurate and reliable sea-ice class information for sea-ice analysis.


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