Computer vision and sensor fusion for detecting buried objects
Conference
·
OSTI ID:10159214
Given multiple images of the surface of the earth from dual-band infrared sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. Supervised learning pattern classifiers (including neural networks,) are used. We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing information from multiple sensor types. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved problem of detecting buried land mines from an airborne standoff platform.
- Research Organization:
- Lawrence Livermore National Lab., CA (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 10159214
- Report Number(s):
- UCRL-JC-112103; CONF-9210231-4; ON: DE93012605
- Resource Relation:
- Conference: 26. annual Asilomar conference on signals, systems, and computers,Pacific Grove, CA (United States),25-30 Oct 1992; Other Information: PBD: Oct 1992
- Country of Publication:
- United States
- Language:
- English
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