Target detection using supervised machine learning algorithms for GPR data
Tóm tắt
A novel approach of supervised machine learning technique is used in this paper to identify landmines. The work presented here has two contributions. First contribution is three features (major axis, minor axis and principal component analysis) based performance comparison of two machine learning technique: support vector machine classifier and neural network classifier. In the second contribution, a new method of extracting five features (mean, variance, kurtosis, skewness and entropy) is suggested. Support vector machine and neural network classifier are trained on three and five-feature data-set. Collection of ground penetrating radar images with surrogate landmines is done in our lab and a data-base of different feature set is created. In experiments, many surrogate mines and non-mines are considered at various depths for data collection. The performance of classifiers is compared on training and testing data-set. Out of the two classifiers, neural network classifier results with better accuracy of 85–90% for training data samples in both (three feature and five feature) analysis. Two trained classifiers are tested over twenty cases of unseen samples. Neural network classifier gives better results of 5–10% increased accuracy than support vector machine classifier over test set also.
Tài liệu tham khảo
Zhu, Q., & Collins, L. M. (2005). Application of feature extraction methods for landmine detection using the Wichmann/Niitek ground-penetrating radar. IEEE Transactions on Geoscience and Remote Sensing, 43(1), 81–85.
Park, S., Kim, K., & Ko, K. H. (2014). Multi-feature based multiple landmine detection using ground penetrating radar. Radio Engineering, 23(2), 643–651.
Ko, K. H., Jang, G., Park, K., & Kim, K. (2013). GPR-based landmine detection and identification using multiple features. International Journal of Antennas and Propagation. https://doi.org/10.1155/2012/826404.
Nishimoto, M., Ueno, S., & Kimura, Y. (2006). Feature extraction from GPR data for identification of landmine-like objects under rough ground surface. Journal of Electromagnetic Waves and Applications, 20(12), 1577–1586.
Park, K., Park, S., Kim, K., & Ko, K. H. (2013). Multi feature based detection of landmines using ground penetrating radar. Progress in Electromagnetics Research, 134, 455–474.
Lu, Q., Pu, J., & Liu, Z. (2014). Feature extraction and automatic material classification of underground objects from ground penetrating radar data. Hindawi Publishing Corporation Journal of Electrical and Computer Engineering. https://doi.org/10.1155/2014/347307.
Bishop, C. M. (2007). Pattern recognition and machine learning (1st ed.). New York: Springer.
Nath, B., & Bhuiyan, A. (2007). A geometrical feature-based sensor fusion model of GPR and IR for detection and classification of anti-personnel mines. In Proceedings of the 7th international conference on intelligent systems design and applications ISDA '07, pp. 849–856.
Huang, X., & Zhang, L. (2007). Classification and extraction of spatial features in urban areas using high-resolution multi spectral imagery. IEEE Geoscience and Remote Sensing Letters, 4(2), 260–264.
Torrione, P., & Collins, L. M. (2007). Texture features for antitank landmine detection using ground penetrating radar. IEEE Transactions on Geoscience and Remote Sensing, 45(2), 2374–2382.
Savelyev, T. G., Van Kempen, L., Sahli, H., Sachs, J., & Sato, M. (2007). Investigation of time–frequency features for GPR landmine discrimination. IEEE Transactions on Geoscience and Remote Sensing, 45(1), 118–129.
Sun, Y., & Li, J. (2003). Time–frequency analysis for plastic landmine detection via forward-looking ground penetrating radar. IEE Proceedings, Radar, Sonar and Navigation, 150(4), 253–261.
Queiroz, F. A. A., Vieira, D. A. G., & Travassos, X. L. (2013). Analyzing the relevant features of GPR scattered waves in time- and frequency-domain. Research in non-destructive evaluation, 24(2), 105–123.
Smitha, N., Singh, V., & Sridhara, S. N. (2016). Pyramidal horn antenna for ground penetrating radar application. In IEEE (INDICON), pp. 1–6.