A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging

doi:10.3390/s24154994

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Journal Article > Item 987654321/126940

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/126940

Title:	A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging
Authors:	Chiu, Chien-ching;Lin, Che-yu;Chi, Yu-jen;Hsu, Hsiu-hui;Chen, Po-hsiang
Keywords:	electromagnetic imaging;artificial intelligence;anisotropic objects;back-propagation scheme;loss function;convolutional neural network
Date:	2024-08
Issue Date:	2025-03-20 09:31:59 (UTC+8)
Publisher:	MDPI
Abstract:	In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions.
Relation:	Sensors 24(15),　4994
DOI:	10.3390/s24154994
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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