Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Shafie, B.M.; Moallem, Payman; Sabahi, Mohamad Farzan

doi:10.1049/iet-rsn.2019.0423

Cited by 3 publications

(1 citation statement)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, a discriminative dictionary is learned for SRC. In our previous work [4], some virtual dictionaries were established through FDDL used independently in several SRCs. The SRC results were then combined in a parallel decision fusion (PDF) structure to recognise the target.…”

mentioning

confidence: 99%

Optimal decision fusion using sparse representation‐based classifiers on monogenic‐signal dictionaries for SAR ATR

2020

Self Cite

View full text Add to dashboard Cite

Sparse representation-based classification (SRC) possesses remarkable characteristics for application in synthetic aperture radar (SAR) automatic target recognition (ATR), for instance, inherent feature extraction and robustness to articulation, and so on. However, the performance of SRC is highly sensitive to parameters such as sufficient training samples, SAR images quality, and targets' changing conditions in depression, pose, configuration, and so on. Unfortunately, the training sample resources for SAR ATR are often expensive and scarce. Further, the targets in SAR images, even with slightest changes in conditions, display mutable characteristics attributable to unique SAR image formation, and speckle noise corruption. To overcome these obstacles, this Letter proposes to establish several compact and complementary dictionaries using monogenic signal's components of SAR images and Fisher discriminative dictionary learning. Then, an optimal decision fusion (ODF) strategy is proposed, which utilises SRC and the latter dictionaries for robust SAR ATR. Compared with single classifiers or multiple parallel classifiers, the proposed ODF increases the accuracy of recognition, while at the same time, decreases the complexity of the system. The proposed methods have considerable features making them applicable in practical situations. Based on the experimental results, the proposed methods outperform state-of-theart approaches.

show abstract

mentioning

confidence: 99%

Optimal decision fusion using sparse representation‐based classifiers on monogenic‐signal dictionaries for SAR ATR

2020

Self Cite

View full text Add to dashboard Cite

show abstract

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

Chen

2020

IEEE Access

View full text Add to dashboard Cite

With the rapid development of computer networks and multimedia technologies, images, which are important carriers of information dissemination, have made human cognition of things easier. Image recognition is a basic research task in computer vision, multimedia search, image understanding and other fields. This paper proposes a hierarchical feature learning structure that is completely automatically based on the original pixels of the image, and uses the K-SVD (K-Singular Value Decomposition) algorithm with label consistency constraints to train the discriminant dictionary. For different types of image data sets, the algorithm only extracts image blocks. After dense sampling, an efficient OMP (Orthogonal Matching Pursuit) encoder is used to obtain a layered sparse representation. The improved SIFT (Scale Invariant Feature Transform) algorithm is used to solve the difficult problem of multimedia visual image stereo matching. The feature point extraction and stereo matching of multimedia visual images, different scales and different viewpoint images are analyzed separately. Aiming at a large number of low-dimensional geometric features of 3D images, this paper studies the extraction and sorting strategies of low-dimensional geometric features of 3D images. A sparse representation method for 3D images is proposed, and the sparseness of image features is evaluated. This further improves the accuracy of 3D image representation and the robustness of 3D image recognition algorithms.

show abstract

Target Recognition Method Based on Corner Feature

Zhang

Shang

2022

2022 International Conference on Artificial Intelligence in Everything (AIE)

View full text Add to dashboard Cite

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Cited by 3 publications

References 76 publications

Optimal decision fusion using sparse representation‐based classifiers on monogenic‐signal dictionaries for SAR ATR

Optimal decision fusion using sparse representation‐based classifiers on monogenic‐signal dictionaries for SAR ATR

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

Target Recognition Method Based on Corner Feature

Contact Info

Product

Resources

About