A Study on the Performance of Magnetic Material Identification System by SIFT-BRISK and Neural Network Methods

• Published in: IEEE transactions on magnetics Vol. 51; no. 8; pp. 1 - 16
• Main Authors: Ege, Yavuz, Nazlibilek, Sedat, Kakilli, Adnan, Citak, Hakan, Kalender, Osman, Karacor, Deniz, Erturk, Korhan Levent, Sengul, Gokhan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; BRISK; Educational institutions; Ground penetrating radar; Identification; Invariants; Magnetic anomalies; Magnetic field measurement; Magnetic fields; Magnetic materials; Magnetic resonance imaging; Magnetism; Mine detection; Neural networks; PCA; Platforms; Remote sensing systems; Sensors; SIFT; mine detection; scale-invariant feature transform (SIFT); identification; Binary robust invariant scalable keypoint (BRISK); principal component analysis (PCA); neural networks
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2015.2408572

Industry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained.