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| Content Provider | IET Digital Library |
|---|---|
| Author | Pan, Ting Peng, Dong Yang, Xiangli Huang, Pingping Yang, Wen |
| Abstract | (Dis)similarity measures play an important role in the interpretation of polarimetric synthetic aperture radar (PolSAR) images. Here, the authors introduce a kind of similarity measures for PolSAR images based on the concepts of Hölder pseudo-divergence and Hölder divergence. Authors’ similarity measures are more generalised as the derived formulas indicate that they contain several widely used measures, such as Bartlett or Bhattahcharyya distances and Chernoff distance. Also, their similarity measures are derived from the complex Wishart distribution, so these measures are good at quantifying the similarity between two covariance matrices and perform well while dealing with classification problems for PolSAR images. Experimental results on unsupervised and supervised classification of PolSAR images also verify the effectiveness of their measures. |
| Starting Page | 7593 |
| Ending Page | 7596 |
| Page Count | 4 |
| Volume Number | 2019 |
| e-ISSN | 20513305 |
| Issue Number | Issue 21, Nov (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/21 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0635 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-07-11 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Algebra Bartlett Distances Bhattahcharyya Distances Chernoff Distance Complex Wishart Distribution Covariance Matrices Hölder Pseudodivergence Image Classification Image Recognition Polarimetric Synthetic Aperture Radar Image Classification Radar Equipment Radar Imaging Radar Polarimetry Supervised PolSAR Image Classification System And Application Unsupervised PolSAR Image Classification |
| Content Type | Text |
| Resource Type | Article |
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