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A Magnetic Measurement System and Identification Method for Buried Magnetic Materials Within Wet and Dry Soils

BROWSE_DETAIL_CREATION_DATE: 31-05-2018

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BROWSE_DETAIL_TYPE: Article

BROWSE_DETAIL_PUBLISH_STATE: Published

BROWSE_DETAIL_FORMAT: No File

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Ege, Yavuz (Author), Nazlıbilek, Sedat (Author), Kakilli, Adnan (Author), Çıtak, Hakan (Author), Kalender, Osman (Author), Şengül, Gökhan (Author), Karaçor, Deniz (Author),

BROWSE_DETAIL_CONTRIBUTERS: Ertürk, Korhan Levent (Research Responsible),


BROWSE_DETAIL_PUBLICATION_NAME: IEEE Transactions On Geoscience And Remote Sensing BROWSE_DETAIL_PUBLICATION_NUMBER: 3 BROWSE_DETAIL_PUBLICATION_VOLUME: 54 BROWSE_DETAIL_PUBLICATION_PAGE: 1803-1811


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Image matching, magnetic materials, mine detection, resistive sensors, speeded-up feature transform (SURF) algorithm. 


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In this paper, a new magnetic measurement system is developed to determine upper surfaces of buried magnetic materials, particularly land mines. This measurement system uses the magnetic-anomaly-detection method. It also has intelligent identification software based on an image matching algorithm. It is aimed to determine and identify the buried ferromagnetic materials with minimum energy consumption. It is concentrated on the detection and identification of the shapes of upper surfaces of buried magnetic materials in dry and wet conditions. The effect of humidity in the detection process for detection is tested. In this paper, we used sensor images to identify various ferromagnetic materials and similar objects. Sensor images of soils at various humidities covering the objects were obtained. We used the speeded-up-feature-transform algorithm in the comparison process of the images. Dry soil sample images match with the corresponding wet soil samples with the highest matching rate. The images for different objects can easily be distinguished by the matching process.



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BROWSE_DETAIL_TAB_REFERENCES[1] L. Robledo, M. Carrasco, and D. Mery, “A survey of land mine detectiontechnology,” Int. J. Remote Sens., vol. 30, no. 9, pp. 2399–2410,Jan. 2009.[2] O. Missaoui, H. Frigui, and P. Gader, “Land-mine detection with groundpenetratingradar using multistream discrete hidden Markov models,”IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2080–2099,Jun. 2011.[3] G. Ramachandran, P. D. Gader, and N. Wilson, “GRANMA: Gradientangle model algorithm on wideband EMI data for land-mine detection,”IEEE Trans. Geosci. Remote Sens. Lett., vol. 7, no. 3, pp. 535–539,Jul. 2010.[4] J. Deans, J. Gerhard, and L. J. Carter, “Analysis of a thermal imagingmethod for landmine detection, using infrared heating of the sand surface,”Infrared Phys. Technol., vol. 48, no. 3, pp. 202–216, Aug. 2006.[5] J. E. McFee et al., “A comparison of fast inorganic scintillators for thermalneutron analysis landmine detection,” IEEE Trans. Nuclear Sci., vol. 56,no. 3, pp. 1584–1592, Jun. 2009.[6] V. Bom, M. A. Ali, and C. W. E. van Eijk, “Land mine detection withneutron back scattering imaging using a neutron generator,” IEEE Trans.Nuclear Sci., vol. 53, no. 1, pp. 356–360, Feb. 2006.[7] S. Yuk, K. H. Kim, and Y. Yi, “Detection of buried landmine with x-raybackscatter technique,” Nuclear Instrum. Methods Phys., vol. 568, no. 1,pp. 388–392, Nov. 2006.[8] S. Nazlibilek, O. Kalender, and Y. Ege, “Mine identification and classifi-cation by mobile sensor network using magnetic anomaly,” IEEE Trans.Instrum. Meas., vol. 60, no. 3, pp. 1028–1036, Mar. 2011.[9] J. Vyhnanek, M. Janosek, and P. Ripka, “AMR gradiometer for minedetection,” Sens. Actuators A, Phys., vol. 186, pp. 100–104, Oct. 2012.[10] M. Woloszyn, “Detection of ferromagnetic objects in local anomaly ofthe Baltic Sea,” Polish Maritime Research, vol. 15, no. 2, pp. 77–82,Jul. 2008.[11] S. Hübschmann and M. Schneider, “Magnetoresistive sensor, principles ofoperation and applications,” Zetex Appl. Note, vol. 20, pp. 1–10, 1996.[12] D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int.J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.[13] Y. Ke and R. Sukthankar, “PCA-SIFT: A more distinctive representationfor local image descriptors,” in Proc. IEEE Conf. Comput. Vis. PatternRecognit., 2004, pp. 511–517.[14] H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robustfeatures,” in Proc. 9th Eur. Conf. Comput. Vis., 2006, vol. 3951,pp. 404–417.[15] L. Juan and O. Gwon, “A comparison of SIFT, PCA-SIFT and SURF,”Int. J. Image Process., vol. 3, no. 4, pp. 143–152, 2009.[16] K. Ashish, G. Suman, T. G. Vijay, and V. Eldho, “Degradation ofkresoxim-methyl in soil: Impact of varying moisture, organic matter, soilsterilization, soil type, light and atmospheric CO2 level,” Chemosphere,vol. 111, pp. 209–217, Sep. 2014.


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