Target Recognition of SAR Images Based on Azimuthal Constraint Reconstruction

Miao, Xin; Liu, Yunlong

doi:10.1155/2021/9974723

Cited by 4 publications

(5 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the presence of noise, they cannot be completely equal in real images. en, there is a difference between the template and the image, as follows [16]:…”

Section: Line Segment Least Squares Template Matching Least Squares I...mentioning

confidence: 99%

Application of CAD Technology in Extracting Line Feature of Industrial Part Image

2022

Journal of Control Science and Engineering

View full text Add to dashboard Cite

In order to solve the problem that the properties of the acquired images in engineering are often poor and the features of the images are seriously lost, so that the image features cannot be effectively developed, an application method of CAD technology in the extraction of linear features of industrial parts images is proposed. Based on 3D computer-aided design (CAD) data, by solving the parameter transformation relationship between the CAD 3D Cartesian coordinate system and the image coordinate system, the target line feature is projected onto the image, and the initial value range of the image line feature is obtained. The edge point sequence is extracted by multiplication template matching, and the generated point sequence is fitted by the least square method to obtain the line equation of the image line feature. By solving the intersection of the intersecting lines of the image, the straight line segment representing the contour of the industrial part can be obtained with high precision. The experimental results show that the above algorithm is used to extract the line features of real industrial part images, and the median error of the difference between the reconstructed line length and the line length measured by the vernier caliper is 0.232 mm. The practice has proved that CAD technology has a certain auxiliary and guiding role in the research of industrial part image feature extraction algorithms. It can be applied to high-precision image measurement and reverse engineering of regular industrial parts. In engineering technology, it can help improve the automation level of industrial production.

show abstract

“…Due to the presence of noise, they cannot be completely equal in real images. en, there is a difference between the template and the image, as follows [16]:…”

Section: Line Segment Least Squares Template Matching Least Squares I...mentioning

confidence: 99%

Application of CAD Technology in Extracting Line Feature of Industrial Part Image

2022

Journal of Control Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Electromagnetic features, including scattering centers and azimuthal sensitivity, were useful for discriminating different targets in SAR images 18 – 24 In Refs. 18–22, attributed scattering centers were extracted from SAR images for target recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In Refs. 23 and 24, the azimuthal sensitivity image was constructed by comparing subapertures in SAR images. The classifier works as a decision engine to analyze the features and confirm the target type.…”

Section: Introductionmentioning

confidence: 99%

Synthetic aperture radar target recognition based on adaptive decision fusion of multiple views

Liang

2024

J. Electron. Imag.

View full text Add to dashboard Cite

For the synthetic aperture radar (SAR) automatic target recognition problem, we propose a multiview method based on adaptive decision fusion. Considering the possible independence and correlation among multiview SAR images, a clustering algorithm is first performed to divide them into different view sets. In each view set, the included SAR images are assumed to be highly correlated, which can be effectively classified through joint sparse representation. For the output decisions from different view sets, they are assumed to be relatively independent and contribute disproportionally to the recognition result. Therefore, based on the decisions of individual view sets, adaptive weights are decided based on Shannon entropy theory, which are used to linearly fuse the multiple decisions to determine the target label. The MSTAR dataset is employed in experiments, in which both the standard operating condition and some representative extended operating conditions are set up. The performance of the proposed method is studied in comparison with representative state-of-the-art methods.

show abstract

“…When going to the phase of classifcation, the decision rules are developed for the extracted features. In general, most of present SAR ATR methods directly made use of achievements in the feld of pattern recognition, including the nearest neighbor (NN) [30], support vector machine (SVM) [51,52], adaptive boosting (AdaBoost) [53], and sparse representation-based classifcation (SRC) [54][55][56]. In [51], SVM was frst used for SAR ATR by Zhao and Principe, which became the most prevalent classifer in this feld afterwards.…”

Section: Introductionmentioning

confidence: 99%

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

Weng

2023

Scientific Programming

View full text Add to dashboard Cite

A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to jointly represent them. According to the resulted reconstruction errors of different classes, the target label of test sample can be classified. The moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up the standard operating condition (SOC) and several extended operating conditions (EOCs) including configuration variants, depression angle variances, noise corruption, and partial occlusion to test and validate the proposed method. The results confirm the effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR references.

show abstract

Target Recognition of SAR Images Based on Azimuthal Constraint Reconstruction

Cited by 4 publications

References 52 publications

Application of CAD Technology in Extracting Line Feature of Industrial Part Image

Application of CAD Technology in Extracting Line Feature of Industrial Part Image

Synthetic aperture radar target recognition based on adaptive decision fusion of multiple views

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

Contact Info

Product

Resources

About