Statistics – Applications
Scientific paper
Oct 1997
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3164...59g&link_type=abstract
Proc. SPIE Vol. 3164, p. 59-69, Applications of Digital Image Processing XX, Andrew G. Tescher; Ed.
Statistics
Applications
1
Scientific paper
We report on an evaluation study of a ship classifier based on the principal components analysis (PCA). A set of ship profiles are used to build a covariance matrix which is diagonalized using the Karhunen-Loeve transform. A subset of the principal components corresponding to the highest eigenvalues are selected as the ship features space. The recognition process consists in projecting a profile on this eigen-subspace and performing a similarity measure. We have measured the recognition performance of the classifier using various sets of range-profile signatures of ship silhouette images and simulated synthetic aperture radar images of ships under various aspect angles. It is found that the PCA-based ship classifier design offers good class discriminacy when trained with a limited number of ship classes under an aspect angle range of 60 degrees about the ship side view. Additional tests are however necessary to validate the classifier on large data sets and real images.
Gagnon Langis
Gouaillier Valerie
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