Computer vision for locating buried objects
Given two registered images of the earth, measured with aerial dual-band infrared (IR) sensors, we use advanced computer vision/automatic target recognition techniques to estimate the positions of buried land mines. The images are very difficult to interpret, because of large amounts of clutter. Conventional techniques use single-band imagery and simple correlations. They rely heavily on the judgment of the human doing the interpretation, and give unsatisfactory results with difficult data sets of the type we analyzed. Our automatic algorithms are able to eliminate most of the clutter and give greatly improved indications of regions in the image that could be interpreted as mines. The novelty of our approach lies in the following aspects: (1) a patented data fusion technique using two IR images and physical principles based on Planck's law, (2) a new region-based texture segmentation algorithm using Gabor Transform features and a clustering/thresholding algorithm based on a neural network (Self-Organizing Feature Map), (3) Prior knowledge of measured feasible temperatures and emissivities, and (4) results with real data using buried surrogate mines.
- Research Organization:
- Lawrence Livermore National Lab., CA (United States)
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 5231536
- Report Number(s):
- UCRL-JC-107626; CONF-9111101-2; ON: DE92013979
- Resource Relation:
- Conference: Asilomar conference on signals, systems and computers, Pacific Grove, CA (United States), 4-6 Nov 1991
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
EXPLOSIVES
REMOTE SENSING
AERIAL SURVEYING
ALGORITHMS
AUTOMATION
DETECTION
IMAGE PROCESSING
IMAGES
INFRARED THERMOGRAPHY
UNDERGROUND
LEVELS
MATHEMATICAL LOGIC
MEASURING METHODS
PROCESSING
THERMOGRAPHY
450100* - Military Technology
Weaponry
& National Defense- Chemical Explosions & Explosives