Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1585-1592, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1585-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1585-1592, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1585-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Sep 2017

14 Sep 2017

ICE WATER CLASSIFICATION USING STATISTICAL DISTRIBUTION BASED CONDITIONAL RANDOM FIELDS IN RADARSAT-2 DUAL POLARIZATION IMAGERY

Y. Zhang1,3, F. Li1,2,3, S. Zhang1,3, W. Hao1,3, T. Zhu2, L. Yuan1,3, and F. Xiao1,3 Y. Zhang et al.
  • 1Chinese Antarctic Center of Surveying and Mapping, Wuhan University, 430079 Wuhan, China
  • 2The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079 Wuhan, China
  • 3Collaborative Innovation Centre for Territorial Sovereignty and Maritime Rights, Wuhan University, 430079 Wuhan, China

Keywords: Sea Ice Classification, SAR, Conditional Random Fields, Statistical Distribution Model

Abstract. In this paper, Statistical Distribution based Conditional Random Fields (STA-CRF) algorithm is exploited for improving marginal ice-water classification. Pixel level ice concentration is presented as the comparison of methods based on CRF. Furthermore, in order to explore the effective statistical distribution model to be integrated into STA-CRF, five statistical distribution models are investigated. The STA-CRF methods are tested on 2 scenes around Prydz Bay and Adélie Depression, where contain a variety of ice types during melt season. Experimental results indicate that the proposed method can resolve sea ice edge well in Marginal Ice Zone (MIZ) and show a robust distinction of ice and water.