Detection of mines using hyperspectral remote sensors and detection algorithms

Edwin M. Winter

doi:10.1117/12.484922

11 September 2003 Detection of mines using hyperspectral remote sensors and detection algorithms

Edwin M. Winter

Proceedings Volume 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII; (2003) https://doi.org/10.1117/12.484922
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Edwin M. Winter "Detection of mines using hyperspectral remote sensors and detection algorithms", Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); https://doi.org/10.1117/12.484922

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available