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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pasolli, E. Melgani, F. Donelli, M. |
| Copyright Year | 2004 |
| Abstract | Recently, a promising pattern-recognition system has been presented to deal with the extraction of buried-object characteristics in ground-penetrating-radar images. In particular, it allows the detecting of buried objects by means of a search method based on genetic algorithms and the recognizing of the material type of the identified objects through a classification approach based on support vector machines. In this letter, we propose to extend the processing capabilities of this system by addressing the issue of the detected buried-object size estimation. This problem is viewed as a regression issue where it is aimed at reproducing the relationship between a set of opportunely extracted features and the object size. For such purpose, it is formulated within a Gaussian process (GP) regression approach. A detailed experimental study is reported, showing encouraging object-size-estimation accuracies even when buried objects are close to each other. |
| Sponsorship | IEEE Geoscience and Remote Sensing Society |
| Starting Page | 141 |
| Ending Page | 145 |
| Page Count | 5 |
| File Size | 360009 |
| File Format | |
| ISSN | 1545598X |
| Volume Number | 7 |
| Issue Number | 1 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Gaussian processes Buried object detection Ground penetrating radar Feature extraction Object detection Pattern recognition Shape Genetic algorithms Support vector machines Support vector machine classification pattern recognition Buried objects feature extraction Gaussian-process (GP) regression ground-penetrating radar (GPR) image analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology |
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