Semantic Information Discovery and Complex-Valued Deep Architectures for SAR Data Processing

Asiyabi, Reza Mohammadi; Datcu, Mihai; Anghel, Andrei; Nies, Holger

doi:10.18690/um.3.2022.19

Cited by 3 publications

(8 citation statements)

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“…The hamming AG 0.9631 0.0344 0.0000 0.0000 0.0016 0.0010 0.0000 FR 0.0493 0.9502 0.0001 0.0000 0.0000 0.0004 0.0000 HD 0.0000 0.0000 0.7910 0.0186 0.1045 0.0859 0.0000 HR 0.0207 0.0000 0.1950 0.7427 0.0415 0.0000 0.0000 LD 0.0845 0.1140 0.1343 0.0000 0.4385 0.2287 0.0000 IR 0.0034 0.2029 0.0034 0.0000 0.0073 0.7828 0.0000 WR 0.0068 0.0000 0.0000 0.0000 0.0000 0.0000 0.9932 window is used to extract three overlapping subapertures, covering different portions of the spectrum in the azimuth direction. Later the Inverse Fourier Transform is used to obtain the CV subaperture images [22]. For further explanation on the azimuth subaperture technique refer to [22].…”

Section: Case Study 2 Cv-sar and Azimuth Subaperture Classificationmentioning

confidence: 99%

“…Later the Inverse Fourier Transform is used to obtain the CV subaperture images [22]. For further explanation on the azimuth subaperture technique refer to [22].…”

Section: Case Study 2 Cv-sar and Azimuth Subaperture Classificationmentioning

confidence: 99%

“…Asiyabi et al [21] carried out a comprehensive comparison between the CV-and RV-CNN for PolSAR data classification and demonstrated the superiority of the CV-CNN. In another recent study, Asiyabi et al [22] evaluated the significance of the coherency preservation for CV deep networks. Qian et al [23] proposed the γ -Net to mimic the iterative optimization step in sparse reconstruction of the CV Learned Iterative Shrinkage Thresholding Algorithm (CV-LISTA) for Super resolving SAR Tomographic Inversion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Complex-Valued End-to-End Deep Network With Coherency Preservation for Complex-Valued SAR Data Reconstruction and Classification

Asiyabi

Datcu

Anghel

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Deep learning models have achieved remarkable success in many different fields and attracted many interests. Several researchers attempted to apply deep learning models to Synthetic Aperture Radar (SAR) data processing, but it did not have the same breakthrough as the other fields, including optical remote sensing. SAR data are in complex domain by nature and processing them with Real-Valued (RV) networks neglects the phase component which conveys important and distinctive information. A Complex-Valued (CV) end-to-end deep network is developed in this study for the reconstruction and classification of CV-SAR data. Azimuth subaperture decomposition is utilized to incorporate physics-aware attributes of the CV-SAR into the deep model. Moreover, the correlation coefficient amplitude (Coherence) of the CV-SAR images depends on the SAR system characteristics and physical properties of the target. This coherency should be considered and preserved in the processing chain of the CV-SAR data. The coherency preservation of the CV deep networks for CV-SAR images, which is mostly neglected in the literature, is evaluated in this study. Furthermore, a large-scale CV-SAR annotated dataset for the evaluation of the CV deep networks is lacking. A semantically annotated CV-SAR dataset from Sentinel-1 Single Look Complex StripMap mode data (S1SLC_CVDL dataset) is developed and introduced in this study. The experimental analysis demonstrated the better performance of the developed CV deep network for CV-SAR data classification and reconstruction in comparison to the equivalent RV model and more complicated RV architectures, as well as its coherency preservation and physics-aware capability.

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Section: Case Study 2 Cv-sar and Azimuth Subaperture Classificationmentioning

confidence: 99%

“…Later the Inverse Fourier Transform is used to obtain the CV subaperture images [22]. For further explanation on the azimuth subaperture technique refer to [22].…”

Section: Case Study 2 Cv-sar and Azimuth Subaperture Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Complex-Valued End-to-End Deep Network With Coherency Preservation for Complex-Valued SAR Data Reconstruction and Classification

Asiyabi

Datcu

Anghel

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…The CV model should maintain this correlation to properly preserve and extract the physical information from CV-SAR data [11], [16]. Moreover, the complex correlation coefficient (coherence) of the CV-SAR data conveys important physical properties of the target and SAR system, and should be preserved in the complex model [12]. As a result, a fully CV network with coherence preservation is used in this study [11].…”

Section: Complex-valued Deep Networkmentioning

confidence: 99%

“…One of the main drawbacks of the deep learning-based compression methods is that SLC SAR data is in complex domain by nature, whereas most of the developed deep learning models are in real domain [11]. Applying the realvalued deep models to the complex-valued SAR data, disregards the phase information and only exploits the amplitude of the SAR data [11], [12]. In order to tackle this problem and to exploit the amplitude and phase components of the Complex-Valued (CV) SAR data, CV deep architectures have been developed in a number of studies [11]- [15].…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Autoencoder for Multi-Polarization SLC SAR Data Compression with Side Information

Asiyabi,

Anghel,

Rizzoli

et al. 2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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Recent advances in Synthetic Aperture Radar (SAR) sensors have enabled the acquisition of very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. As a result of the significant increase of SAR data size, an effective compression of the acquired data is of paramount importance. However, conventional data compression methods demonstrate limited effectiveness when applied to SAR data. In order to tackle this problem, in this study, a Complex-Valued (CV) end-to-end deep learning-based architecture based on convolutional autoencoders is proposed to compress Single Look Complex (SLC) SAR data. By relying on dual polarization SAR data, one of the polarization channels of the data is used as the side information to assist the reconstruction of the compressed channel with lower data loss. The obtained results demonstrate the remarkable potential and capability of CV deep learning-based methods for SAR data compression.

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Synthetic Aperture Radar (SAR) for Ocean: A Review

Asiyabi,

Ghorbanian,

Tameh

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Oceans cover approximately 71% of the Earth's surface and provide numerous services to the environment and humans. Precise, real-time, and large-scale monitoring of the oceanographic parameters is essential for ocean conservation and understanding the interactions between oceans and the atmosphere. In this regard, Synthetic Aperture Radar (SAR) systems, with unique capabilities (e.g., day-night and almost allweather data acquisition), provide valuable datasets for ocean studies. Many studies have exploited the applications of SAR imagery for oceans and have proposed numerous methods to study oceanographic parameters. In this study, a brief introduction to SAR and the interaction between microwave signals and the ocean surface are initially provided. Then, the important spaceborne and airborne SAR systems for oceanographic applications are summarized. Subsequently, 12 different applications of SAR systems in the ocean are comprehensively discussed, and the advantages and disadvantages of SAR systems for ocean studies are extensively explored. Finally, the research trend on SAR applications in the ocean is provided by analyzing all the relevant papers published between 1973 and the end of December 2022, and the existing challenges are discussed for future studies.

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Semantic Information Discovery and Complex-Valued Deep Architectures for SAR Data Processing

Cited by 3 publications

References 0 publications

Complex-Valued End-to-End Deep Network With Coherency Preservation for Complex-Valued SAR Data Reconstruction and Classification

Complex-Valued End-to-End Deep Network With Coherency Preservation for Complex-Valued SAR Data Reconstruction and Classification

Complex-Valued Autoencoder for Multi-Polarization SLC SAR Data Compression with Side Information

Synthetic Aperture Radar (SAR) for Ocean: A Review

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