An investigation of the performances of polarimetric target decompositions using GB-SAR imaging

Demirci, Şevket; Özdemir, Caner

doi:10.26833/ijeg.665175

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…For instance, in [8,9], authors extract polarization properties from various terrain targets, while in [10], singlepolarization and dual-polarization radar data are compared in the hail detection. Recently, investigations of various polarization in GBSAR applications have also shown potential, for example, when dealing with circular polarizations [11,12] or using polarimetric information for target classification [13].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification

Turčinović,

Kačan,

Bojanjac

et al. 2024

Sensors

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In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data.

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Section: Introductionmentioning

confidence: 99%

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification

Turčinović,

Kačan,

Bojanjac

et al. 2024

Sensors

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“…Synthetic aperture radar (SAR) is one of the most important imaging systems, since radar is independent of weather and environmental conditions [1][2][3]. High-resolution and wide-swath (HRWS) imaging have been pursued by numerous scholars in recent years.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Echo Separation Scheme for Space-Time Waveform-Encoding SAR Based on Digital Beamforming and Blind Source Separation

et al. 2022

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To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions.

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“…Synthetic aperture radar (SAR) polarimetry is a widely used technique for the derivation of qualitative and quantitative physical information of land [1][2][3][4][5]. Among polarimetric SAR (PolSAR) systems, quad-polarimetric (quad-pol) SAR can obtain complete polarimetric scattering information, and has been successfully applied in terrain classification [6], crop parameter inversion [7], ship detection [8], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Refined Model for Quad-Polarimetric Reconstruction from Compact Polarimetric Data

et al. 2022

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As a special dual-polarization technique, compact polarimetric (CP) synthetic aperture radar (SAR) has already been widely studied and installed on some spaceborne systems due to its superiority to quad-polarization; moreover, quad-pol information can be explored and reconstructed from the CP SAR data. In this paper, a refined model is proposed to estimate the quad-pol information for the CP mode. This model involves CP decomposition, wherein the polarization degree is introduced as the volume scattering model parameter. Moreover, a power-weighted model for the co-polarized coherence coefficient is proposed to avoid the iterative approach in pseudo-quad-pol information reconstruction. Experiments were implemented on the simulated Gaofen-3 and ALOS-2 data collected over San Francisco. Compared with typical reconstruction models, the proposed refined model shows its superiority in estimating the quad-pol information. Furthermore, terrain classification experiments using a complex-value convolutional neural network (CV-CNN) were performed on AIRSAR Flevoland data to validate the reconstruction effectiveness for classification applications.

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An investigation of the performances of polarimetric target decompositions using GB-SAR imaging

Cited by 7 publications

References 33 publications

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification

An Advanced Echo Separation Scheme for Space-Time Waveform-Encoding SAR Based on Digital Beamforming and Blind Source Separation

A Refined Model for Quad-Polarimetric Reconstruction from Compact Polarimetric Data

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