Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

Artiemjew, Piotr; Chojka, Agnieszka; Rapiński, Jacek

doi:10.3390/rs13010007

“…From visualizing the contaminated SAR image, the image has reduced quality with distortion at certain pulses due to the interference. Accordingly, RFI can be identified and detected from the final SAR image by applying any of the recognized detection approaches [10], [11].…”

Section: B Case Ii: Sar Data Contaminated With Awgnmentioning

confidence: 99%

SEMUS - An Open-Source RF-Level SAR Emulator for Interference Modelling in Spaceborne Applications

Hendy,

Kurnia,

Kraus

et al. 2023

Preprint

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<p>Earth observation has a crucial role in understanding and monitoring our planet's health and changes. Spaceborne Synthetic Aperture Radar (SAR) has become a valuable technology for Earth monitoring, leading to a massive expansion of satellite launches. However, within the limited radio frequency (RF) band, Radio Frequency Interference (RFI) poses a significant challenge for SAR technology. RFI can have a significant impact on the overall system performance and particularly on SAR image quality. To analyze and solve the interference problem, a simulator/emulator is required at the RF level to emulate and analyze the effects of different RFI sources on the final focused spaceborne SAR image. This paper presents an open-source RF-level SAR emulator for spaceborne applications called SEMUS. SEMUS is an integrated end-to-end framework for realistic spaceborne SAR scenarios that can generate raw RF data (Level-0) for an arbitrary scene and reconstruct the final SAR-focused image (Level-1). Moreover, the emulator is capable of injecting arbitrary RFI waveforms into the raw SAR data. The simulation results prove SEMUS's ability to generate high-quality Level-0 SAR data above Melbourne, Australia. Affirming its capability, SEMUS is able to reconstruct Level-1 free of RFI or contaminated with interference.</p>

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“…From visualizing the contaminated SAR image, the image has reduced quality with distortion at certain pulses due to the interference. Accordingly, RFI can be identified and detected from the final SAR image by applying any of the recognized detection approaches [10], [11].…”

Section: B Case Ii: Sar Data Contaminated With Awgnmentioning

confidence: 99%

SEMUS - An Open-Source RF-Level SAR Emulator for Interference Modelling in Spaceborne Applications

Hendy,

Kurnia,

Kraus

et al. 2023

Preprint

1

0

View full text Add to dashboard Cite

<p>Earth observation has a crucial role in understanding and monitoring our planet's health and changes. Spaceborne Synthetic Aperture Radar (SAR) has become a valuable technology for Earth monitoring, leading to a massive expansion of satellite launches. However, within the limited radio frequency (RF) band, Radio Frequency Interference (RFI) poses a significant challenge for SAR technology. RFI can have a significant impact on the overall system performance and particularly on SAR image quality. To analyze and solve the interference problem, a simulator/emulator is required at the RF level to emulate and analyze the effects of different RFI sources on the final focused spaceborne SAR image. This paper presents an open-source RF-level SAR emulator for spaceborne applications called SEMUS. SEMUS is an integrated end-to-end framework for realistic spaceborne SAR scenarios that can generate raw RF data (Level-0) for an arbitrary scene and reconstruct the final SAR-focused image (Level-1). Moreover, the emulator is capable of injecting arbitrary RFI waveforms into the raw SAR data. The simulation results prove SEMUS's ability to generate high-quality Level-0 SAR data above Melbourne, Australia. Affirming its capability, SEMUS is able to reconstruct Level-1 free of RFI or contaminated with interference.</p>

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“…In recent years, with the development of the Internet and artificial intelligence, artificial intelligence has been commercialized and scaled up in the fields of security, finance, customer service, retail, net medical and health, advertising and marketing, education, urban transportation, manufacturing, and agriculture [1][2][3]. Artificial intelligence is a strategic technology that leads the future, and the world's major developed countries take the development of artificial intelligence as a major strategy to enhance national competitiveness and maintain national security.…”

Section: Introductionmentioning

confidence: 99%

Research on Image Recognition Methods Based on Deep Learning

Xu,

Li,

Wang

2023

Applied Mathematics and Nonlinear Sciences

0

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In this paper, deep learning is used to study image recognition techniques. Firstly, the image recognition process is structured, the YOLOv4 network framework is constructed, the features are extracted using the PANet reinforcement network, and the image overlap is extracted using the loss function. Then, we make an improved architecture ACDNet algorithm based on YOLOv4 and set the main function of the ACDNet model. Finally, the accuracy of image recognition under different algorithms and the recognition effect evaluation of the ACDNet algorithm are tested, respectively. The study shows that the image recognition accuracy of the ACDNet algorithm is located in the first of the three algorithms, with the highest accuracy of 98.16%, which is good and effective for image recognition and classification. The accuracy of ACDNet in the training set of plant image recognition is 99.34%, which is good for classification and recognition performance.

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Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

Cited by 20 publications

References 41 publications

SEMUS - An Open-Source RF-Level SAR Emulator for Interference Modelling in Spaceborne Applications

SEMUS - An Open-Source RF-Level SAR Emulator for Interference Modelling in Spaceborne Applications

SEMUS - An Open-Source RF-Level SAR Emulator for Interference Modelling in Spaceborne Applications

Research on Image Recognition Methods Based on Deep Learning

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