Texture Representation of SAR Sea Ice Imagery Using Multi-Displacement Co-Occurrence Matrices

In this paper, we desribe mdti-displacement cooccurrence matrices for re~enting W ice textures of SAR imagery. Our design of co-occcurrence matrices captures local relationships among neighboring pixels and global links among distant pixels, an advantage over other existing versions of co-occurrence matrices. As a result, it can adequately represent micro textures, such as grainy details, and macro textures, such as patchy blocks. We have conducted experiments to compare our multi-displacement co-occurrence matrices with other existing versions using Bayesian linear discrimination. We have found that our design is the most texturally representative in terms of classification accuraci= in both training and test datasets. In addition, we have applied this design to sea ice texture analysis which includes detection and localization, and subsequent image-texture mapping.