Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel pattern-recognition system to identify and classify buried objects from ground-penetrating radar (GPR) imagery. The entire process is subdivided into four steps. After a preprocessing step, the GPR image is thresholded to put under light the regions containing potential objects. The third step of the system consists of automatically detecting the objects in the obtained binary image by means of a search of linear/hyperbolic patterns formulated within a genetic optimization framework. In the genetic optimizer, each chromosome models the apex position and the curvature associated with the candidate pattern, while the fitness function expresses the Hamming distance between that pattern and the binary image content. Finally, in the fourth step, the problem of the recognition of the material type of the identified objects is approached as a classification issue, which is solved by means of an opportune feature-extraction strategy and a support vector machine classifier. To illustrate the performances of the proposed system, we conducted a thorough experimental study based on GPR images generated by a GPR simulator based on the finite-difference time-domain method so as to construct different acquisition scenarios by varying the number of buried objects, their position, their size, their shape, and their material type. In general, the obtained experimental results show that the proposed system exhibits promising performances both in terms of object detection and material recognition. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TGRS.2009.2012701 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
buried object detection,feature extraction,finite difference time-domain analysis,geophysical techniques,geophysics computing,ground penetrating radar,image classification,support vector machines,GPR image analysis,binary image,buried objects classification,chromosome models,feature-extraction strategy,finite-difference time-domain method,genetic optimization framework,ground-penetrating radar,linear/hyperbolic patterns,material recognition method,objects detection,pattern-recognition system,support vector machine classifier,Buried objects,feature extraction,genetic algorithms (GAs),ground-penetrating radar (GPR),pattern recognition,support vector machine (SVM) | Ground-penetrating radar,Binary image,Remote sensing,Artificial intelligence,Contextual image classification,Computer vision,Object detection,Pattern recognition,Support vector machine,Fitness function,Feature extraction,Hamming distance,Mathematics | Journal |
Volume | Issue | ISSN |
47 | 7 | 0196-2892 |
Citations | PageRank | References |
42 | 2.71 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edoardo Pasolli | 1 | 285 | 17.04 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Massimo Donelli | 3 | 76 | 6.83 |