SAR Target Configuration Recognition via Two-Stage Sparse Structure Representation

International Journal of Antennas and Propagation

Chen

et al. 2020

Self Cite

Dictionary construction is a key factor for the sparse representation-(SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. e proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. e accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Product Dictionary Learning-Based SAR Target Configuration Recognition

International Journal of Antennas and Propagation

Chen

et al. 2020

Self Cite

“…Synthetic aperture radar (SAR) works regardless of light and weather conditions, and observes the Earth’s surface all day [ 1 , 2 ]. SAR is widely applied in various civil and military fields such as resource exploration, ecological environment monitoring, climate change research, military mapping, and military reconnaissance [ 3 , 4 , 5 ]. With the continuous developments of SAR technology, automatic target recognition (ATR) [ 6 , 7 , 8 ] of SAR images has attracted great attention over the years.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification

Zhang

Tan

et al. 2018

Sensors

In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively by a regularized non-negative sparse learning method. Second, a synthesis dictionary and an analysis dictionary are jointly learned from the corresponding training subset located in the aspect sector. By doing so, the local aspect dictionary pair is obtained. Finally, the class label of the testing sample is inferred by a use of the minimum reconstruction residual under the representation with the local aspect dictionary pair. Using the local aspect sector training subset rather than the global aspect training set reduces the interference of a large amount of unrelated training samples, which leads to a more discriminative local aspect dictionary pair for target classification. The experiments are conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, and the results demonstrate that the proposed approach is effective and superior to the state-of-the-art methods.

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

Chen

2022

Radio Science

Self Cite

A multi‐manifold based sparse representation (MMSR) algorithm which can preserve the local structure of the datasets in the reconstruction space with multiple descriptions is proposed for synthetic aperture radar (SAR) target configuration recognition. Different from the traditional manifold learning‐based algorithms that preserve the local structure of the datasets in the low‐dimensional space, the proposed MMSR guarantees local structure preserving of the datasets after feature extraction and sparse representation. The proposed MMSR can avoid corrupted relationships of the samples to ease the obstacle of target aspect angle sensitivity, which can realize more accurate SAR target configuration recognition. Satisfying results are obtained on the moving and stationary target acquisition and recognition database.