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@INPROCEEDINGS{Ghosh:1031804,
author = {Ghosh, Raktim and Delilbasic, Amer and Cavallaro, Gabriele
and Bovolo, Francesca},
title = {{A} {CNN} {A}rchitecture {T}ailored {F}or {Q}uantum
{F}eature {M}ap-{B}ased {R}adar {S}ounder {S}ignal
{S}egmentation},
publisher = {IEEE},
reportid = {FZJ-2024-05824},
pages = {442-445},
year = {2024},
abstract = {This article presents a hybrid quantum-classical framework
by incorporating quantum feature maps into a classical
Convolutional Neural Network (CNN) architecture for
detecting different subsurface targets in radar sounder
signals. The quantum feature maps are generated by quantum
circuits to utilize spatially-bound input information from
the training samples. The associated spectral probabilistic
amplitudes of the feature maps are further fed into the
classical CNN-based network to classify the subsurface
targets in the radargram. Experimental results on the MCoRDS
and MCoRDS3 datasets demonstrated the capability of
enhancing the classical architecture through quantum feature
maps for characterizing radar sounder data.},
month = {Jul},
date = {2024-07-07},
organization = {IGARSS 2024 - 2024 IEEE International
Geoscience and Remote Sensing
Symposium, Athens (Greece), 7 Jul 2024
- 12 Jul 2024},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511)},
pid = {G:(DE-HGF)POF4-5111},
typ = {PUB:(DE-HGF)8},
UT = {WOS:001316158500102},
doi = {10.1109/IGARSS53475.2024.10642188},
url = {https://juser.fz-juelich.de/record/1031804},
}