Robust azimuth ambiguity detection method in SAR images based on non-negative matrix factorization

doi:10.23919/JSEE.2026.000067

Abstract

Abstract:

Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images. Sub-look spectral analysis (SSA) is a common ambiguity-detection method, but its performance is limited by threshold sensitivity and the high correlation of specific ambiguities across sub-looks. To overcome these specific limitations, this paper proposes an improved detection method. It first increases the number of sub-looks and constructs a high-dimensional multi-look matrix to enrich the coherence differences between targets and ambiguities. Non-negative matrix factorization is then employed to decompose this matrix, effectively separating the coherent target components from the variably coherent ambiguity components without relying on predefined thresholds. Experimental results on real data demonstrate that the proposed improvements achieve superior azimuth-ambiguity-detection performance compared with conventional SSA methods.

Key words: synthetic aperture radar (SAR), azimuth ambiguity, multi-look processing, nonnegative matrix factorization (NMF)

Jieshuang LI, Mingliang TAO, Lei CUI, Yanyang LIU, Ling WANG. Robust azimuth ambiguity detection method in SAR images based on non-negative matrix factorization[J]. Journal of Systems Engineering and Electronics, 2026, 37(3): 743-754.

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References 30

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Method	Index	ROI
Method	Index	A	B	C
LR (mean + 0×std)	MD	782.20	884.22	361.45
	SDD	469.40	605.57	216.59
	VCV	27.80	49.65	47.11
	MCD	589.22	648.87	266.10
LR (mean + 0.5×std)	MD	581.36	1316.19	250.88
	SDD	377.34	886.91	188.17
	VCV	46.04	96.99	6.14
	MCD	410.23	977.49	181.49
LR (mean + 1×std)	MD	1238.74	1370.68	384.22
	SDD	836.58	913.51	222.56
	VCV	114.21	72.18	4.58
	MCD		1017.55	277.87
NMF (K=2)	MD	620.29	1182.59	265.07
	SDD	388.78	824.23	176.96
	VCV	16.82	47.22	3.97
	MCD	448.81	877.00	204.38
NMF (K=4)	MD	478.53	1216.10	154.71
	SDD	334.53	843.43	106.71
	VCV	2.53	31.17	2.84
	MCD	312.76	930.04	111.78
NMF (K=6)	MD	637.60	1201.63	165.70
	SDD	481.65	830.68	118.56
	VCV	23.98	46.06	3.63
	MCD	441.09	899.76	112.98
NMF (K=8)	MD	882.43	1199.35	163.64
	SDD	688.98	833.18	116.04
	VCV	54.99	50.15	2.36
	MCD	602.84	891.63	117.46

Method	Index	ROI D
LR (mean+0.5×std)	FAR	0
LR (mean+0.5×std)	MAR	25
LR (mean+1×std)	FAR	4.76
LR (mean+1×std)	MAR	18.75
NMF (K=2)	FAR	0
NMF (K=2)	MAR	43.75
NMF (K=4)	FAR	0
NMF (K=4)	MAR	0
NMF (K=6)	FAR	0
NMF (K=6)	MAR	0
NMF (K=8)	FAR	0
NMF (K=8)	MAR	0

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