DipSAR: Deep Image Prior for Sparse Sampled Near-Field SAR Millimeter-Wave Imaging

Rawin Assabumrungrat; Nakorn Kumchaiseemak; Jianping Wang; Dingyang Wang; Phoom Punpeng; Francesco Fioranelli; Theerawit Wilaiprasitporn

doi:10.1109/SENSORS56945.2023.10325198

DipSAR: Deep Image Prior for Sparse Sampled Near-Field SAR Millimeter-Wave Imaging

Rawin Assabumrungrat, Nakorn Kumchaiseemak, Jianping Wang, Dingyang Wang, Phoom Punpeng, Francesco Fioranelli^*, Theerawit Wilaiprasitporn

^*Corresponding author for this work

Microwave Sensing, Signals & Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

91 Downloads (Pure)

Abstract

We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. Our proposed DipSAR model recovers missing samples from sparse data and reconstructs the SAR image using a conventional method. In this study, we utilize an existing SAR dataset and create fourteen different patterns to generate additional sparse samples by removing certain data points. We then evaluate the performance of DipSAR in comparison to the conventional method. The results show that DipSAR outperforms the conventional method in terms of the intersection over union (IoU) score.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE SENSORS
Place of Publication	Piscataway
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	979-8-3503-0387-2
ISBN (Print)	979-8-3503-0388-9
DOIs	https://doi.org/10.1109/SENSORS56945.2023.10325198
Publication status	Published - 2023
Event	2023 IEEE SENSORS, SENSORS 2023 - Vienna, Austria Duration: 29 Oct 2023 → 1 Nov 2023

Publication series

Name	Proceedings of IEEE Sensors
ISSN (Print)	1930-0395
ISSN (Electronic)	2168-9229

Conference

Conference	2023 IEEE SENSORS, SENSORS 2023
Country/Territory	Austria
City	Vienna
Period	29/10/23 → 1/11/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

deep image prior
millimeter-wave
near-field imaging
sparse data
synthetic aperture radar

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10.1109/SENSORS56945.2023.10325198

DipSAR_Deep_Image_Prior_for_Sparse_Sampled_Near-Field_SAR_Millimeter-Wave_ImagingFinal published version, 821 KB

Cite this

@inproceedings{ddbb29f4a05249f18e44f7d0c5252ea8,

title = "DipSAR: Deep Image Prior for Sparse Sampled Near-Field SAR Millimeter-Wave Imaging",

abstract = "We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. Our proposed DipSAR model recovers missing samples from sparse data and reconstructs the SAR image using a conventional method. In this study, we utilize an existing SAR dataset and create fourteen different patterns to generate additional sparse samples by removing certain data points. We then evaluate the performance of DipSAR in comparison to the conventional method. The results show that DipSAR outperforms the conventional method in terms of the intersection over union (IoU) score.",

keywords = "deep image prior, millimeter-wave, near-field imaging, sparse data, synthetic aperture radar",

author = "Rawin Assabumrungrat and Nakorn Kumchaiseemak and Jianping Wang and Dingyang Wang and Phoom Punpeng and Francesco Fioranelli and Theerawit Wilaiprasitporn",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2023 IEEE SENSORS, SENSORS 2023 ; Conference date: 29-10-2023 Through 01-11-2023",

year = "2023",

doi = "10.1109/SENSORS56945.2023.10325198",

language = "English",

isbn = "979-8-3503-0388-9",

series = "Proceedings of IEEE Sensors",

publisher = "IEEE",

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address = "United States",

}

Assabumrungrat, R, Kumchaiseemak, N, Wang, J, Wang, D, Punpeng, P, Fioranelli, F & Wilaiprasitporn, T 2023, DipSAR: Deep Image Prior for Sparse Sampled Near-Field SAR Millimeter-Wave Imaging. in Proceedings of the 2023 IEEE SENSORS. Proceedings of IEEE Sensors, IEEE, Piscataway, 2023 IEEE SENSORS, SENSORS 2023, Vienna, Austria, 29/10/23. https://doi.org/10.1109/SENSORS56945.2023.10325198

DipSAR: Deep Image Prior for Sparse Sampled Near-Field SAR Millimeter-Wave Imaging. / Assabumrungrat, Rawin; Kumchaiseemak, Nakorn; Wang, Jianping et al.
Proceedings of the 2023 IEEE SENSORS. Piscataway: IEEE, 2023. (Proceedings of IEEE Sensors).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - DipSAR

T2 - 2023 IEEE SENSORS, SENSORS 2023

AU - Assabumrungrat, Rawin

AU - Kumchaiseemak, Nakorn

AU - Wang, Jianping

AU - Wang, Dingyang

AU - Punpeng, Phoom

AU - Fioranelli, Francesco

AU - Wilaiprasitporn, Theerawit

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. Our proposed DipSAR model recovers missing samples from sparse data and reconstructs the SAR image using a conventional method. In this study, we utilize an existing SAR dataset and create fourteen different patterns to generate additional sparse samples by removing certain data points. We then evaluate the performance of DipSAR in comparison to the conventional method. The results show that DipSAR outperforms the conventional method in terms of the intersection over union (IoU) score.

AB - We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. Our proposed DipSAR model recovers missing samples from sparse data and reconstructs the SAR image using a conventional method. In this study, we utilize an existing SAR dataset and create fourteen different patterns to generate additional sparse samples by removing certain data points. We then evaluate the performance of DipSAR in comparison to the conventional method. The results show that DipSAR outperforms the conventional method in terms of the intersection over union (IoU) score.

KW - deep image prior

KW - millimeter-wave

KW - near-field imaging

KW - sparse data

KW - synthetic aperture radar

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DO - 10.1109/SENSORS56945.2023.10325198

M3 - Conference contribution

AN - SCOPUS:85179756621

SN - 979-8-3503-0388-9

T3 - Proceedings of IEEE Sensors

BT - Proceedings of the 2023 IEEE SENSORS

PB - IEEE

CY - Piscataway

Y2 - 29 October 2023 through 1 November 2023

ER -

DipSAR: Deep Image Prior for Sparse Sampled Near-Field SAR Millimeter-Wave Imaging

Abstract

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Keywords

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