A method of using geomagnetic anomaly to recognize objects based on HOG and 2D-AVMD

Abstract

In order to identify the shape of underground small magnetic anomaly objects, we use Support Vector Machines (SVM) to identify the underground magnetic anomaly targets. Firstly, as the SVM needs a lot of training data, and we also need to make full use of the magnetic field signal, nine component signals including total magnetic intensity (TMI) and five independent components of tensor are calculated from the original detected magnetic signal. Secondly, the nine component signals are subjected respectively to two-dimensional adaptively variational mode decomposition (2D-AVMD), which is advanced based on the two indicators, namely Mutual information (MI) and empirical entropy (EE), and we can get the nine primary signals from the decomposition results of nine component signals called the Intrinsic Mode Function (IMF). Then, the Histogram of Oriented Gradients (HOG) of the nine primary signals is extracted, and the feature data would be constructed into feature vectors. In the end, Support Vector Machines (SVM) are adopted to process these feature vectors. The output of the SVM can indicate the result of small objects’ shape recognition under the ground. Experiments prove that the shape recognition accuracy of underground small magnetic anomaly object recognition reaches 90%.

Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

“…Most of these traditional detection methods are based on the detection of a single feature, which cannot make full use of the information in the signal, and are often only suitable for specific environmental noise. In recent years, many scholars have studied the method of using machine learning to extract multi-domain features of target signals to achieve target detection [10]- [13]. This kind of method can establish a more complex nonlinear relationship between the multi-domain features of the signal, so as to better ensure the detection effect under different noises.…”

Detection and Classification of Multi-Magnetic Targets Using Mask-RCNN

“…J. Zheng [13] uses singular value decomposition (SVD) to extract magnetic anomaly signal features and uses the support vector machine (SVM) to classify magnetic source targets. In the same year, J. Zheng [14] used 2D-AVMD to decompose the magnetic anomaly signal, extracted the hog features of the signal, and deployed the SVM method to classify and recognize the two kinds of cylinder and disk magnetic targets respectively. However, the above two…”