Gray-statistics-based Twin Feature Extraction for Hyperbola Classification in Ground Penetrating Radar images
| Autor: | Feng Zhao, Da Yuan, Zhiyong An |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Dimensionality reduction Feature extraction 020206 networking & telecommunications 02 engineering and technology Row vector Hyperbola Robustness (computer science) Ground-penetrating radar Statistics 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Entropy (information theory) 020201 artificial intelligence & image processing General Environmental Science |
| Zdroj: | Procedia Computer Science. 147:567-573 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2019.01.215 |
| Popis: | The row vector and column vector of a ground penetrating radar (GPR) B-scan image have different physical meanings, and the features of heterogeneous medium properties based on these vectors can provide new possibilities for hyperbola classification. This study uses the features of both row and column vectors (i.e., twin vectors), gray statistics, and united coding to produce a twin gray statistics sequence (TGSS), a representation of the GPR image, based on information entropy. An actual dataset and multiple classification methods are used to compare and evaluate the robustness and dimension reduction performance of TGSS. The results show that the proposed method has relatively favorable robustness and steady dimension reduction performance in the test environment with a small number of samples and class imbalance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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