A High-Frequency Multipeak Model for Wide-Angle SAR Imagery

Fuller, Dane F.; Saville, Michael A.

doi:10.1109/tgrs.2012.2226732

Cited by 30 publications

(13 citation statements)

References 32 publications

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“…The filter essentially selects the local maximum from a 3 × 3 sliding window. In [22], a combination of the sliding window filter and threshold are used to extract the scattering centres shown in Fig. 2.…”

Section: Asc Model and Extractionmentioning

confidence: 99%

Commercial vehicle classification from spectrum parted linked image test‐attributed synthetic aperture radar imagery

Saville

Saini

Smith

2016

IET Radar, Sonar & Navigation

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Section: Asc Model and Extractionmentioning

confidence: 99%

Commercial vehicle classification from spectrum parted linked image test‐attributed synthetic aperture radar imagery

Saville

Saini

Smith

2016

IET Radar, Sonar & Navigation

Self Cite

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“…In Reference [11], the study reveals the characteristic of civilian cars in SAR imaging with noise and classifies ten kinds of vehicles under various signal-to-noise ratio (SNR) conditions. Fuller et al established a high-frequency model to solve the problems of parameter estimation and scatter classification in the spatial domain with simulated data [12]. In addition, the literature [13] established a forward approach of parametric scattering center model in the ATR system which had achieved great agreement between the simulation and experimental data.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition

Pang

Dai

et al. 2018

Electronics

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Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in “Science”, one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods.

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“…Because of its importance, some publications have addressed the methods of extracting anisotropic characteristics. In [4][5][6][7], the extraction of anisotropic characteristics was achieved via the parameter estimation of the parametric scattering models. In these publications, the attributed scattering center model (ASCM) [8][9][10] was used to represent the scattering behavior.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], the authors used the modified RELAX algorithm to extract ASCM parameters of scattering centers. The high-frequency multipeak model was derived from the ASCM and was used to describe the scattering behavior in the SAR imagery in [5]. This model was used for joint frequency-polarization scatter classification.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Anisotropic Characteristics of Scattering Centers and Feature Enhancement in Wide-Angle SAR Imagery Based on the Iterative Re-Weighted Tikhonov Regularization

et al. 2018

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The anisotropic characteristic reflects discriminating features of the geometry of a scattering center. In this study, we propose a novel method based on the iterative re-weighted Tikhonov regularization (IRWTR) to achieve the extraction of anisotropic characteristics of scattering centers in the wide-angle SAR synthetic aperture radar (SAR) imaging. Moreover, based on the extracted anisotropic scattering behaviors, the incomplete imaging results of the distributed scattering centers are restored. In this paper, we first discussed the scattering property in SAR imagery from the perspective of attributed scattering center model (ASCM). The reason for the incomplete imaging results of the distributed scattering centers was also discussed based on the ASCM. Subsequently, we modeled the aspect-dependent amplitude responses of a scattering center as a linear combination of a set of orthogonal basis. Consequently, the extraction of anisotropic characteristics can be transformed into an inverse problem, which was solved by the proposed IRWTR with high efficiency and accuracy. After the extraction, we attempted to restore the complete image of a distributed scattering center, which consisted of only two points. The enhanced SAR image can reveal the actual shape of a target. Processing results of electromagnetic computation data validated that the proposed method is effective and efficient.

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A High-Frequency Multipeak Model for Wide-Angle SAR Imagery

Cited by 30 publications

References 32 publications

Commercial vehicle classification from spectrum parted linked image test‐attributed synthetic aperture radar imagery

Commercial vehicle classification from spectrum parted linked image test‐attributed synthetic aperture radar imagery

Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition

Extraction of Anisotropic Characteristics of Scattering Centers and Feature Enhancement in Wide-Angle SAR Imagery Based on the Iterative Re-Weighted Tikhonov Regularization

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