River SAR image segmentation using L1 norm based hybrid active contours

doi:10.11947/j.AGCS.2024.20220690

Abstract

Abstract:

To solve the problem that the existing active contour models are difficult to segment river SAR images accurately, this paper presents a hybrid active contour model based on the L₁ norm. First, the median values of the pixel intensities in the inner and outer regions of the contour curve are calculated as the region fitting centers to suppress the influence of the interference regions in SAR images on their accuracies. Second, the L₁ norm is used to construct a new energy constraint term and the edge indicator function is introduced into the model's energy functional to further enhance the segmentation performance. Finally, the median and mean energy constraint terms based on the L₁ norm are combined and additional region-fitting center constraint terms are added to improve the overall stability of the model. The segmentation experiments on real river SAR images show that the proposed model can segment river SAR images more accurately and stably than the existing models.

Key words: river segmentation, SAR image, active contour model, hybrid energy term, L₁ norm

CLC Number:

P237

Yibo XING, Bin HAN, Bingkun BAO. River SAR image segmentation using L₁ norm based hybrid active contours[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(8): 1598-1609.

Figures/Tables 5

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Tab.1

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References 26

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模型	图像1		图像2		图像3		图像4		图像5		图像6		图像7		图像8		图像9		图像10
模型	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率
CV模型	52.48	75.81	83.56	49.18	78.97	28.74	65.77	91.92	68.42	80.27	72.11	68.60	47.53	91.25	46.78	80.79	61.75	77.32	51.29	83.68
GLSPF模型	62.27	62.98	64.87	67.99	68.55	41.09	58.26	94.24	54.05	87.77	61.42	75.98	55.43	90.14	56.56	77.40	66.78	74.70	58.76	81.31
FGLFE模型	74.68	55.69	75.56	62.34	78.32	28.68	64.21	92.26	63.97	76.95	74.56	65.36	51.89	90.61	50.29	79.72	60.95	77.61	54.42	82.92
CE模型	84.48	26.22	89.15	38.16	83.14	25.27	73.68	90.47	75.17	76.95	82.56	57.69	81.18	79.17	69.41	70.92	71.89	71.42	65.90	79.20
MSCE模型	85.34	24.68	91.49	30.78	86.61	21.99	82.50	87.17	77.84	73.91	86.72	50.53	84.75	75.52	87.97	47.89	72.29	70.60	74.94	72.70
WL₁L_P模型	98.34	2.31	94.30	19.42	98.81	1.13	93.42	70.02	88.89	63.35	94.01	28.02	93.69	52.13	94.78	25.83	90.91	44.12	90.11	53.45
LT模型	72.68	59.68	70.17	65.18	78.12	29.91	74.22	90.16	57.23	84.11	76.01	64.13	52.01	90.16	50.19	80.01	61.10	77.31	55.60	82.59
THFC模型	86.68	21.23	92.62	23.86	86.32	22.56	95.68	68.26	93.68	42.68	93.68	28.84	92.56	63.69	88.02	49.62	92.02	39.35	88.35	58.68
本文模型	98.55	1.04	98.52	2.12	97.17	2.58	97.83	11.93	98.31	6.37	98.65	2.41	98.54	6.97	97.52	4.22	97.25	5.79	98.02	6.22

模型	图像1	图像2	图像3	图像4	图像5	图像6	图像7	图像8	图像9	图像10
CV模型	1.97	1.23	0.74	1.83	0.81	1.21	2.56	1.81	2.32	2.96
GLSPF模型	5.58	3.86	3.02	4.82	3.21	4.76	7.65	5.32	6.87	9.16
FGLFE模型	4.06	2.45	2.08	3.76	2.06	4.22	5.32	4.01	4.87	6.03
CE模型	2.49	1.55	1.13	2.79	1.22	3.68	3.86	2.20	2.94	4.54
MSCE模型	2.89	1.64	1.34	3.25	1.43	4.03	4.12	2.89	3.54	5.13
WL₁L_P模型	1.54	1.17	1.02	1.18	0.75	1.15	2.20	1.27	2.39	1.92
LT模型	3.86	2.27	1.98	3.31	1.98	3.75	4.86	3.21	4.21	5.72
THFC模型	5.03	3.35	2.16	3.89	2.23	4.25	6.71	4.13	5.68	7.11
本文模型	1.67	1.16	1.01	1.13	0.68	1.02	2.13	1.07	2.01	1.98

图像	RDU－Net模型		AU－Net模型		DAU－Net模型		本文模型
图像	准确率	虚警率	准确率	虚警率	准确率	虚警率	准确率	虚警率
图像1	84.09	26.54	86.01	19.56	88.49	15.00	98.55	1.04
图像2	97.49	8.48	97.70	1.99	98.10	2.67	98.52	2.12
图像3	93.23	14.24	96.09	8.86	96.52	6.40	97.17	2.58
图像4	96.26	51.02	97.19	36.38	96.93	44.13	97.83	11.93
图像5	96.16	15.43	96.23	12.83	96.33	14.51	98.31	6.37
图像6	93.88	27.44	95.18	19.87	95.53	12.09	98.65	2.41
图像7	94.16	53.36	95.48	43.14	96.50	30.57	98.54	6.97
图像8	92.99	32.40	95.76	9.01	95.96	8.33	97.52	4.22
图像9	96.66	16.28	96.86	13.23	97.16	11.32	97.25	5.79
图像10	94.89	30.76	96.64	15.92	96.56	15.96	98.02	6.22
平均值	93.08	27.80	95.21	17.88	95.91	16.00	98.21	4.97

River SAR image segmentation using L₁ norm based hybrid active contours

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