30 October 1997 Ship silhouette recognition using principal components analysis
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Abstract
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.
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Valerie Gouaillier, Langis Gagnon, "Ship silhouette recognition using principal components analysis", Proc. SPIE 3164, Applications of Digital Image Processing XX, (30 October 1997); doi: 10.1117/12.279572; https://doi.org/10.1117/12.279572
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