Hyperspectral imaging is an important technology for the passive optical detection of surface and buried land mines from an airborne platform. Hyperspectral remote sensing can exploit many different potential mine observables in the visible and infrared portions of the spectrum. The primary surface mine observable is a spectral difference between the mine body and the background. With a high quality VNIR/SWIR hyperspectral sensor, it is possible to detect these mines as spectral anomalies using techniques that have been previously applied to the detection of military targets. Algorithms developed for the military surveillance application can be directly applied to the surface mine problem. In this paper, two different spectral anomaly approaches are explored. The first is a local spectral anomaly detection algorithm, which examines the color of each pixel for differences with its surroundings. The second is a global spectral anomaly detection algorithm that measures the color of each pixel relative to its occurrence in the whole scene. Both algorithms were developed for the problem of detecting military targets in complex backgrounds and are applied here to the problem of detecting surface mines.

Date Published: 11 September 2003
PDF: 6 pages
Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); doi: 10.1117/12.484922

Published in SPIE Proceedings Vol. 5089:
Detection and Remediation Technologies for Mines and Minelike Targets VIII
Russell S. Harmon; John H. Holloway Jr.; J. T. Broach, Editor(s)