| 000 | 05054naaaa2200997uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/76950 | ||
| 005 | 20220220095729.0 | ||
| 020 | _abooks978-3-0365-2150-3 | ||
| 020 | _a9783036521497 | ||
| 020 | _a9783036521503 | ||
| 024 | 7 |
_a10.3390/books978-3-0365-2150-3 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aTB _2bicssc |
|
| 100 | 1 |
_aLópez, Yuri _4edt |
|
| 700 | 1 |
_aFernández, María García _4edt |
|
| 700 | 1 |
_aLópez, Yuri _4oth |
|
| 700 | 1 |
_aFernández, María García _4oth |
|
| 245 | 1 | 0 | _aAdvanced Techniques for Ground Penetrating Radar Imaging |
| 260 |
_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2021 |
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| 300 | _a1 electronic resource (218 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aGround penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
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| 546 | _aEnglish | ||
| 650 | 7 |
_aTechnology: general issues _2bicssc |
|
| 653 | _aGround Penetrating Radar (GPR) | ||
| 653 | _aUnmanned Aerial Vehicles (UAVs) | ||
| 653 | _aSynthetic Aperture Radar (SAR) | ||
| 653 | _aReal Time Kinematic (RTK) | ||
| 653 | _aUltra-Wide-Band (UWB) | ||
| 653 | _alandmine and IED detection | ||
| 653 | _anon-destructive testing | ||
| 653 | _aGPR | ||
| 653 | _acoherence | ||
| 653 | _asemblance | ||
| 653 | _aattribute analysis | ||
| 653 | _aimaging | ||
| 653 | _aGPR trace | ||
| 653 | _ahigh-resolution data | ||
| 653 | _alarge-scale survey | ||
| 653 | _aarchaeological prospection | ||
| 653 | _aGround-Penetrating Radar | ||
| 653 | _avelocity analysis | ||
| 653 | _acoherency functionals | ||
| 653 | _aGPR data processing | ||
| 653 | _aGPR data migration | ||
| 653 | _aspatial-variant convolution neural network (SV-CNN) | ||
| 653 | _aspatial-variant convolution kernel (SV-CK) | ||
| 653 | _aradar image enhancing | ||
| 653 | _aMIMO radar | ||
| 653 | _aneural networks | ||
| 653 | _aimaging radar | ||
| 653 | _aground penetrating radar | ||
| 653 | _awavelet scattering network | ||
| 653 | _amachine learning | ||
| 653 | _asupport vector machine | ||
| 653 | _apipeline identification | ||
| 653 | _asnow | ||
| 653 | _asnow water equivalent (SWE) | ||
| 653 | _astepped-frequency continuous wave radar (SFCW) | ||
| 653 | _asoftware defined radio (SDR) | ||
| 653 | _asnowpack multilayer reflectance | ||
| 653 | _aGround Penetrating Radar | ||
| 653 | _aSynthetic Aperture Radar | ||
| 653 | _alandmine | ||
| 653 | _aImprovised Explosive Device | ||
| 653 | _aradar | ||
| 653 | _anoise attenuation | ||
| 653 | _aGaussian spike impulse noise | ||
| 653 | _adeep convolutional denoising autoencoders (CDAEs) | ||
| 653 | _adeep convolutional denoising autoencoders with network structure optimization (CDAEsNSO) | ||
| 653 | _aapplied geophysics | ||
| 653 | _adigital signal processing | ||
| 653 | _aenhancement of 3D-GPR datasets | ||
| 653 | _aclutter noise removal | ||
| 653 | _aspectral filtering | ||
| 653 | _aground-penetrating radar | ||
| 653 | _anondestructive testing | ||
| 653 | _apipelines detection | ||
| 653 | _amodeling | ||
| 653 | _asignal processing | ||
| 653 | _an/a | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/4541 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/76950 _70 _zDOAB: description of the publication |
| 999 |
_c79967 _d79967 |
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