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Proceedings Paper

Comparison of geometric features for object classification in aerial imagery
Author(s): Jeffrey L. Solka; David A. Johannsen; David J. Marchette; Ron J. Guidry
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Paper Abstract

This paper examines the use of three feature sets for object classification in aerial imagery. The first feature set is based on affine invariant functions of the central moments computed on the objects within the image. The second feature set employed Zernike moment invariants and the third feature set utilized affine invariant functions of the central moments that are computed over a spline fit to the object boundary. The initial object locations were obtained using either a region of interest identification process based on low-level image processing techniques or a hand extraction process. A single nearest neighbor, k-nearest neighbors, and a weighted k-nearest neighbors classifier were employed to evaluate the utility of the various feature sets for both the hand extracted and region of interest identified objects. The performance of the full system is characterized via probability of detection and probability of false alarm.

Paper Details

Date Published: 17 August 2000
PDF: 10 pages
Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); doi: 10.1117/12.395552
Show Author Affiliations
Jeffrey L. Solka, Naval Surface Warfare Ctr. (United States)
David A. Johannsen, Naval Surface Warfare Ctr. (United States)
David J. Marchette, Naval Surface Warfare Ctr. (United States)
Ron J. Guidry, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 4050:
Automatic Target Recognition X
Firooz A. Sadjadi, Editor(s)

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