Comparison of geometric features for object classification in aerial imagery

Jeffrey L. Solka; David A. Johannsen; David J. Marchette; Ron J. Guidry

doi:10.1117/12.395552

17 August 2000 Comparison of geometric features for object classification in aerial imagery

Jeffrey L. Solka, David A. Johannsen, David J. Marchette, Ron J. Guidry

Proceedings Volume 4050, Automatic Target Recognition X; (2000) https://doi.org/10.1117/12.395552
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

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Jeffrey L. Solka, David A. Johannsen, David J. Marchette, and Ron J. Guidry "Comparison of geometric features for object classification in aerial imagery", Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); https://doi.org/10.1117/12.395552

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KEYWORDS

Palladium

Image processing

Image classification

Feature extraction

Genetic algorithms

Image segmentation

Airborne remote sensing

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