Modulus Stretch-Based Circular SAR Imaging with Contour Thinning

Hu, Rongchun; Peng, Zhenming; Zheng, Kelong

doi:10.3390/app9132728

Cited by 3 publications

(9 citation statements)

References 37 publications

(46 reference statements)

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“…The contour thinning imaging algorithm is an improved algorithm based on the backprojection algorithm. The principle and steps of contour thinning imaging algorithm are described in detail in Reference [35]. In this paper, the main steps of the algorithm are given below.…”

Section: Contour Thinning Imagingmentioning

confidence: 99%

“…They published Target Discrimination Research Challenge and the corresponding data subset [30]. Subsequently, different researchers have studied WSAR target recognition [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…where T is the threshold value, k 1 is the enhancement coefficient, and k 2 is the inhibition coefficient. According to Reference [35], the parameters of the modulus stretch function were k 1 = 1.2, k 2 = 0.1, and T = 0.9. It can be seen that the image of all models of vehicles after modulus stretch greatly strengthens the contour features of the target.…”

mentioning

confidence: 99%

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A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Peng

2019

Electronics

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Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.

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Section: Contour Thinning Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Peng

2019

Electronics

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“…Ref. [34] proposed a contour thinning imaging algorithm based on modulus stretch. This algorithm is an improvement on the backprojection algorithm, but can obtain a thinner contour than the traditional backprojection imaging.…”

Section: Introductionmentioning

confidence: 99%

“…The combination target detection and imaging has also appeared in infrared target detection research [55][56][57][58][59][60][61][62]. In the previous work, the authors proposed a contour thinning imaging algorithm to improve target recognizability [34]. The algorithm combines imaging and target recognition.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging

Peng

et al. 2020

Electronics

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The contour thinning algorithm is an imaging algorithm for circular synthetic aperture radar (SAR) that can obtain clear target contours and has been successfully used for circular SAR (CSAR) target recognition. However, the contour thinning imaging algorithm loses some details when thinning the contour, which needs to be improved. This paper presents an improved contour thinning imaging algorithm based on residual compensation. In this algorithm, the residual image is obtained by subtracting the contour thinning image from the traditional backprojection image. Then, the compensation information is extracted from the residual image by repeatedly using the gravitation-based speckle reduction algorithm. Finally, the extracted compensation image is superimposed on the contour thinning image to obtain a compensated contour thinning image. The proposed algorithm is demonstrated on the Gotcha dataset. The convolutional neural network (CNN) is used to recognize the target image. The experimental results show that the image after compensation has a higher target recognition accuracy than the image before compensation.

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Enhanced two dimensional turntable radar imaging using minimal sparse nonconvex optimization

Wang¹,

Wu³

2022

Eighth Symposium on Novel Photoelectronic Detection Technology and Applications

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Modulus Stretch-Based Circular SAR Imaging with Contour Thinning

Cited by 3 publications

References 37 publications

A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging

Enhanced two dimensional turntable radar imaging using minimal sparse nonconvex optimization

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