Multiview Imaging for Low-Signature Target Detection in Rough-Surface Clutter Environment
| Autor: | DaHan Liao, Traian Dogaru, Davide Comite, Moeness G. Amin, Fauzia Ahmad |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Forward-looking ground-penetrating radar (FL-GPR)
image fusion microwave tomography multiview imaging target detection terrain clutter Electrical and Electronic Engineering Earth and Planetary Sciences (all) business.industry Scattering Computer science Interface (computing) Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 020206 networking & telecommunications 02 engineering and technology Image segmentation Interference (wave propagation) law.invention law Radar imaging 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Computational electromagnetics Clutter Computer vision Artificial intelligence Radar business 021101 geological & geomatics engineering |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 55:5220-5229 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2017.2703820 |
| Popis: | Forward-looking ground-penetrating radar (FL-GPR) permits standoff sensing of shallow in-road threats. A major challenge facing this radar technology is the high rate of false alarms stemming from the vulnerability of the target responses to interference scattering arising from interface roughness and subsurface clutter. In this paper, we present a multiview approach for target detection in FL-GPR. Various images corresponding to the different views are generated using a tomographic algorithm, which considers the near-field nature of the sensing problem. Furthermore, for reducing clutter and maintaining high cross-range resolution over the imaged area, each image is computed in a segmentwise fashion using coherent integration over a suitable set of measurements from multiple platform positions. We employ two fusion approaches based on likelihood ratio tests detector to combine the multiview images for enhanced target detection. The superior performance of the multiview approach over single-view imaging is demonstrated using electromagnetic modeling data. |
| Databáze: | OpenAIRE |
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