Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

Andrew Young; Stephen Marshall; Alison Gray

doi:10.1117/12.2213530

12 May 2016 Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

Andrew Young, Stephen Marshall, Alison Gray

Author Affiliations +

Proceedings Volume 9844, Automatic Target Recognition XXVI; 98440S (2016) https://doi.org/10.1117/12.2213530
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

The use of aerial hyperspectral imagery for the purpose of remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of the objects under surveillance. For example, a camouflaged vehicle, deliberately designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared spectrum. This work aims to develop improved target detection methods, using a two-stage approach, firstly by development of a physics-based atmospheric correction algorithm to convert radiance into re ectance hyperspectral image data and secondly by use of improved outlier detection techniques. In this paper the use of the Percentage Occupancy Hit or Miss Transform is explored to provide an automated method for target detection in aerial hyperspectral imagery.

Citation Download Citation

Andrew Young, Stephen Marshall, and Alison Gray "Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques", Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440S (12 May 2016); https://doi.org/10.1117/12.2213530

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