基于形态学自适应分块的高分辨SAR多特征增强算法

doi:10.12305/j.issn.1001-506X.2022.02.15

Abstract

Abstract:

The conventional compressive sensing sparse imaging algorithm of synthetic aperture radar (SAR) based on the least absolute shrinkage and selection operator (LASSO) is easy to lose weak scatters. Although the imaging algorithm based on extended group LASSO is capable of strengthening weak scatters of structure of target of interests, it is inflexible to adaptively block the groups for enhancing the target feature, since only the Euclidean distance is adopted for the blocking operation, and the quality of the SAR imagery is poor. In this paper, a morphological auto-blocking alternating direction method of multipliers (MAB-ADMM) algorithm is proposed for the structural feature enhancement of high-resolution SAR imagery. A mixture of $\ell_{\rm{M}} $/$\ell_{\rm{F}} $ norm and an $\ell_1 $ norm are introduced to represent the structural and sparse features, respectively, and a coordination process is carried out to enhance the features cooperatively. Because the proposed MAB-ADMM algorithm employs the morphological blocking scheme based on the geodesic distance, it is capable of capturing the contour of target of interests effectively, and the accuracy and integrity of the enhancement can be improved. In the experiment, both the simulated and raw SAR data are used for the verification of the proposed algorithm. Comparisons with the conventional algorithm are performed to show the superiority of the proposed algorithm. The phase transition analysis is carried out to evaluate the performance of the proposed algorithm quantitatively. Also, the MSTAR data set is employed to verify the performance of the proposed algorithm in preparing for the SAR target classification.

Key words: alternating direction method of multipliers (ADMM), synthetic aperture radar (SAR) imaging, morphology, phase transition diagram (PTD)

CLC Number:

TN957.52

Cheng FANG, Huijuan LI, Wen LU, Yumeng SONG, Lei YANG. Multi-feature enhancement algorithm for high resolution SAR based on morphological auto-blocking[J]. Systems Engineering and Electronics, 2022, 44(2): 470-479.

Figures/Tables 7

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Table 1

References 30

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类别	分类方法
	PCANet算法		特征增强参数1		特征增强参数2		特征增强参数3		三组参数综合结果
	误分类数	准确率/%	误分类数	准确率/%	误分类数	准确率/%	误分类数	准确率/%	误分类数	准确率/%
SN9563	25	87.18	13	93.33	14	92.82	14	92.82	9	95.38
SN9566	47	76.02	42	78.57	37	81.12	33	83.16	28	85.71
SNC21	40	79.59	36	81.63	30	84.69	35	82.14	27	86.22
SNC71	0	100.0	0	100.0	0	100.0	0	100.0	0	100.0
SN132	29	85.20	22	87.76	20	89.80	19	90.31	12	93.88
SN812	13	93.33	6	96.92	8	95.90	8	95.90	4	97.95
SNS7	16	91.62	10	95.29	12	93.72	11	94.245	8	95.81
合计	170	87.55	129	90.55	121	91.14	120	91.21	88	93.55

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Multi-feature enhancement algorithm for high resolution SAR based on morphological auto-blocking

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