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. 2021 Nov 26:2021:2392642.
doi: 10.1155/2021/2392642. eCollection 2021.

A SAR Target Recognition Method via Combination of Multilevel Deep Features

Junhua Wang  1 , Yuan Jiang  2
Affiliations

A SAR Target Recognition Method via Combination of Multilevel Deep Features

Junhua Wang et al. Comput Intell Neurosci. .

Abstract

For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flowchart of the proposed method.
Figure 2
Figure 2
Images of targets to be classified. (a) BMP2. (b) BTR70. (c) T72. (d) T62. (e) BRDM2. (f) BTR60. (g) ZSU23/4. (h) D7. (i) ZIL131. (j) 2S1.
Figure 3
Figure 3
Confusion matrix achieved by the proposed method.
Figure 4
Figure 4
Average recognition rates under target occlusions.
Algorithm 1
Algorithm 1
Clustering algorithm for deep features.
See this image and copyright information in PMC

References

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