| Title | Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery |
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| Author | Clausi, D A; Yu, B |
| Source | IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers) vol. 42, no. 1, 2004 p. 215-228, https://doi.org/10.1109/TGRS.2003.817218  Open
Access |
| Year | 2004 |
| Alt Series | Natural Resources Canada, Contribution Series 20181382 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing |
| Program | Canada Centre for Remote Sensing Divsion |
| Released | 2004 01 01 |
| Abstract | This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research
comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are
investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window
size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures. |
| GEOSCAN ID | 311736 |
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