# Symmetric Magnetic Anomaly Objects’ Orientation Recognition Based on Local Binary Pattern and Support Vector Machine

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Geomagnetic Gradient Tensor Matrix

## 3. Discrete Wavelet Transform

## 4. Local Binary Pattern Feature

## 5. Simulation and Analysis

## 6. Experimental Verification

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Design of the experiment table which is like a 20 × 20 grid. The picture on the left is the real thing, the picture on the right is the schematic.

**Figure 3.**(

**a**) Basic principle of local binary patern (LBP); (

**b**) three interpreted instances explain texture and count one LBP.

**Figure 4.**Type 1 model’s nine component signals including total magnetic intensity (TMI), Bx, By, Bz, Bxx, Bxy, Bxz, Byy, Byz in MATLAB.

**Figure 5.**This image is an analysis of the LBP from the perspective of each component signal, which illustrates the distinguishing of the six orientations’ LBP feature of the different components. T1 is the feature of Type 1, T2 is the feature of Type 2, and so on. We can compare the same and different characteristics of the six types in the figure. We aimed to analyze the classification ability of LBP features for 6 different orientations.

**Figure 6.**The classification effect of three component signals to distinguish the six orientations in 3D.

**Figure 7.**Discrete wavelet noise reduction effects. (1) shows the TMI signal before discrete wavelet transform (DWT), (2) shows the TMI signal after DWT on the level 1, (3) shows the TMI signal after DWT on the level 2 and (4), (5) show the details of coefficients.

Type | Inclination (°) | Declination (°) |
---|---|---|

1 | 0 | 0 |

2 | 30 | 45 |

3 | 60 | 45 |

4 | 0 | 90 |

5 | 30 | −45 |

6 | 60 | −45 |

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**MDPI and ACS Style**

Zheng, J.; Fan, H.; Li, Z.; Zhang, Q.
Symmetric Magnetic Anomaly Objects’ Orientation Recognition Based on Local Binary Pattern and Support Vector Machine. *Symmetry* **2019**, *11*, 97.
https://doi.org/10.3390/sym11010097

**AMA Style**

Zheng J, Fan H, Li Z, Zhang Q.
Symmetric Magnetic Anomaly Objects’ Orientation Recognition Based on Local Binary Pattern and Support Vector Machine. *Symmetry*. 2019; 11(1):97.
https://doi.org/10.3390/sym11010097

**Chicago/Turabian Style**

Zheng, Jianyong, Hongbo Fan, Zhining Li, and Qi Zhang.
2019. "Symmetric Magnetic Anomaly Objects’ Orientation Recognition Based on Local Binary Pattern and Support Vector Machine" *Symmetry* 11, no. 1: 97.
https://doi.org/10.3390/sym11010097