By Topic

Multisensor data fusion for surface land-mine detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Filippidis, A. ; Div. of Land Oper., Defence Sci. & Technol. Organ., Salisbury, SA, Australia ; Jain, L.C. ; Martin, N.

Receiver operating characteristic (ROC) curves have been used to examine a novel target recognition system using a number of knowledge-based techniques to automatically detect surface land mines that are present in 30 sets of thermal and multispectral images. A summary of the results, graphed at a probability of detection greater than or equal to 96%, shows the false-alarm rates (FARs) obtained using various combinations of fusing sensors and neural classifiers averaged over the 30 images. The results show that using two neural-network classifiers on the input textural and spectral characteristics of selected multispectral bands, we obtained FARs of approximately 3%. Using polarization-resolved images only, we obtained FARs of 1.15%. Fusing the best classifier output with the polarization-resolved images, we obtained FARs as low as 0.023%. This result has shown the large improvement gained in the sensor fusion. Also, an improvement is shown by comparing these results with those reported in an existing commercial system

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:30 ,  Issue: 1 )