Focusing, imaging, and ATR for the Gotcha 2008 wide angle SAR collection

Gianelli, Christopher; Xu, Luzhou

doi:10.1117/12.2015773

Cited by 11 publications

(10 citation statements)

References 2 publications

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Section: Algorithm Comparisonmentioning

confidence: 99%

“…Gianelli et al proposed an imaging algorithm called data-adaptive Sparse Learning via Iterative Minimization (SLIM), and used the algorithm to achieve 90% recognition accuracy [32]. Gianelli et al used basically the same dataset as in this paper, but they eliminated all of the flawed data and used only 540 images in total for the recognition experiment.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

“…In this paper, only the moving and changing image data of the vehicle is excluded, and a total of 660 images are used for the recognition experiment. No comparative experiment was conducted on the same dataset in this paper since it was impossible to know which image data were excluded from the Reference [32].…”

Section: Algorithm Comparisonmentioning

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%

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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: Algorithm Comparisonmentioning

confidence: 99%

Section: Algorithm Comparisonmentioning

confidence: 99%

Section: Algorithm Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Peng

2019

Electronics

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“…Subsequently, many researchers have studied the imaging theory of circular SAR. The Air Force Research Laboratory (AFRL) and other organizations have conducted several experimental tests of WSAR and CSAR and released several test datasets and challenge problems about WSAR and CSAR [6][7][8][9][10][11][12][13]. Zhang et al presented an approximation method using linear spotlight SAR to simulate CSAR [14,15].…”

Section: Introductionmentioning

confidence: 99%

Modulus Stretch-Based Circular SAR Imaging with Contour Thinning

Peng

Zheng³

2019

Applied Sciences

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This paper presents a modulus stretch-based circular Synthetic Aperture Radar (SAR) imaging method. This method improves the traditional backprojection algorithm for circular SAR imaging, and introduces the modulus stretch transformation function in the imaging process. By performing a modulus stretch transformation on the intermediate results, the target contour in the final imaging result is thinner and clearer. A thinner and clearer contour can help to increase the recognizability of the target and provide a basis for subsequent target recognition. The proposed method is demonstrated on the line target imaging simulations and Gothca dataset.

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