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Naslov:Cross-Hole GPR for Soil Moisture Estimation Using Deep Learning
Avtorji:ID Pongrac, Blaž (Avtor)
ID Gleich, Dušan (Avtor)
ID Malajner, Marko (Avtor)
ID Sarjaš, Andrej (Avtor)
Datoteke:.pdf Pongrac-2023-Cross-Hole_GPR_for_Soil_Moisture.pdf (3,22 MB)
MD5: A343C58E748F8AA807F3EF13F6EA6742
 
URL https://www.mdpi.com/2072-4292/15/9/2397
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
Opis:This paper presents the design of a high-voltage pulse-based radar and a supervised data processing method for soil moisture estimation. The goal of this research was to design a pulse-based radar to detect changes in soil moisture using a cross-hole approach. The pulse-based radar with three transmitting antennas was placed into a 12 m deep hole, and a receiver with three receive antennas was placed into a different hole separated by 100 m from the transmitter. The pulse generator was based on a Marx generator with an LC filter, and for the receiver, the high-frequency data acquisition card was used, which can acquire signals using 3 Gigabytes per second. Used borehole antennas were designed to operate in the wide frequency band to ensure signal propagation through the soil. A deep regression convolutional network is proposed in this paper to estimate volumetric soil moisture using time-sampled signals. A regression convolutional network is extended to three dimensions to model changes in wave propagation between the transmitted and received signals. The training dataset was acquired during the period of 73 days of acquisition between two boreholes separated by 100 m. The soil moisture measurements were acquired at three points 25 m apart to provide ground truth data. Additionally, water was poured into several specially prepared boreholes between transmitter and receiver antennas to acquire additional dataset for training, validation, and testing of convolutional neural networks. Experimental results showed that the proposed system is able to detect changes in the volumetric soil moisture using Tx and Rx antennas.
Ključne besede:ground penetrating radar, cross-hole, L-band, deep learning, convolutional neural network, soil moisture estimation
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:13.02.2023
Datum sprejetja članka:26.04.2023
Datum objave:04.05.2023
Založnik:MDPI
Leto izida:2023
Št. strani:Str. 1-17
Številčenje:Letn. 15, Št. 9, št. članka 2397
PID:20.500.12556/DKUM-87960 Novo okno
UDK:681.5
COBISS.SI-ID:153415939 Novo okno
DOI:10.3390/rs15092397 Novo okno
ISSN pri članku:2072-4292
Datum objave v DKUM:03.04.2024
Število ogledov:448
Število prenosov:26
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
:
PONGRAC, Blaž, GLEICH, Dušan, MALAJNER, Marko in SARJAŠ, Andrej, 2023, Cross-Hole GPR for Soil Moisture Estimation Using Deep Learning. Remote sensing [na spletu]. 2023. Vol. 15, no. Št. 9,  članka 2397, p. 1–17. [Dostopano 27 marec 2025]. DOI 10.3390/rs15092397. Pridobljeno s: https://dk.um.si/IzpisGradiva.php?lang=slv&id=87960
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Gradivo je del revije

Naslov:Remote sensing
Skrajšan naslov:Remote sens.
Založnik:MDPI
ISSN:2072-4292
COBISS.SI-ID:32345133 Novo okno

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0065
Naslov:Telematika

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:04.05.2023

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:radarji, globoko učenje, konvolucijske nevronske mreže, ocena vlažnosti tal


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