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001     1031804
005     20250310131232.0
024 7 _ |a 10.1109/IGARSS53475.2024.10642188
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037 _ _ |a FZJ-2024-05824
100 1 _ |a Ghosh, Raktim
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111 2 _ |a IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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245 _ _ |a A CNN Architecture Tailored For Quantum Feature Map-Based Radar Sounder Signal Segmentation
260 _ _ |c 2024
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300 _ _ |a 442-445
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520 _ _ |a 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.
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700 1 _ |a Bovolo, Francesca
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770 _ _ |z 979-8-3503-6032-5
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