An efficient unsupervised technique is proposed for land mine detection from highly cluttered inhomogeneous environment. The proposed technique uses multispectral data for which feature extraction is necessary to classify large volume of data. We applied wavelet based principal component analysis to reduce the dimension of the data as well as to reveal information about target from background clutter. To increase the discrimination between target and clutter a linear transformation of the feature extracted bands is performed. Thereafter, morphological algorithm is used to extract the maximum information about the target. The proposed technique shows excellent detection performance while enhancing the processing speed. Test results using various multispectral data sets show excellent performance and verify the effectiveness of the proposed technique.

Date Published: 29 April 2013
PDF: 10 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 87480Q (29 April 2013); doi: 10.1117/12.2018251

Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)