Flood change detection method using optimized similarity measurement function with temporal-spatial-polarized SAR information

doi:10.11947/j.AGCS.2024.20230355

Abstract

Abstract:

Thanks to its ability for all-weather and all-day observation, synthetic aperture radar (SAR) enables flood monitoring in harsh environments. Currently, flood change detection methods are easily affected by the changes of other ground objects and designed inadequacy for SAR data characteristics. To solve these problems, a novel change detection method using temporal characteristics and flood characteristic distribution is proposed. The proposed method integrates multi-temporal and multi-polarized information to construct temporal-spatial-polarized SAR data. Furthermore, the improved K-means clustering approach for constructed data reduces accumulated errors from different temporal clustering processing. In addition, considering the distribution characteristics of temporal-spatial-polarized SAR data, Cross Entropy is designed to optimize the similarity measurement function to accurately distinguish water body changes caused by flooding. Finally, multi-temporal fully polarimetric Radarsat-2 data from Wuhan and dual polarimetric Sentinel-1 data from Huangmei County in Huanggang are used to validate the effectiveness of the proposed method. The false alarm rate (FA), total errors rate (TE), overall accuracy (OA), and Kappa of our method in Wuhan applied are 5.06%, 5.66%, 94.34%, 0.69 and 1.61%, 2.61%, 97.39%, 0.65, which highlight the advantages of the proposed method. The TE, OA and Kappa of experimental results in Huangmei County have the best performance, which are 1.67%, 98.33% and 0.73. Our method effectively mitigates the effect of changes in other land features on the detection of changes in water bodies. Furthermore, our method not only effectively reduces the impact of other land cover changes but also boasts a swift response capability. It can effectively suppress the influence of urban changes and mountain shadow in flood detection.

Key words: flood, change detection, temporal-spatial-polarized, improved K-means, cross entropy

CLC Number:

P234

Jinqi ZHAO, Yuxuan LI, Zirong LIU, Qing AN, Shiyu SONG, Yufen NIU. Flood change detection method using optimized similarity measurement function with temporal-spatial-polarized SAR information[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(12): 2375-2390.

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方法	FA/（%）	TE/（%）	OA/（%）	Kappa
传统方法+欧氏距离	5.49	7.13	92.87	0.59
传统方法+交叉熵	5.30	6.40	93.60	0.64
扩展方法+KL散度	5.71	6.33	93.67	0.66
扩展方法+JS散度	5.56	6.83	93.17	0.62
扩展方法+卡方距离	5.72	6.69	93.31	0.63
扩展方法+Bray Curtis距离	6.35	8.55	91.45	0.51
本文方法	5.06	5.66	94.34	0.69

方法	FA/（%）	TE/（%）	OA/（%）	Kappa
传统方法+欧氏距离	5.89	6.53	93.47	0.43
传统方法+交叉熵	4.55	5.12	94.88	0.51
扩展方法+KL散度	2.14	2.95	97.05	0.62
扩展方法+JS散度	1.83	2.91	97.09	0.62
扩展方法+卡方距离	8.05	9.57	90.43	0.24
扩展方法+Bray Curtis距离	2.30	4.14	95.86	0.44
本文方法	1.61	2.61	97.39	0.65

数据类别	严东湖				严西湖
数据类别	FA/（%）	TE/（%）	OA/（%）	Kappa	FA/（%）	TE/（%）	OA/（%）	Kappa
HH	5.64	8.51	91.49	0.49	0.88	3.34	96.66	0.40
HV	7.97	8.97	91.03	0.54	10.66	11.66	88.34	0.24
VV	4.73	7.67	92.33	0.51	0.20	2.83	97.17	0.41
HH+HV	5.28	6.92	93.08	0.60	5.80	6.67	93.33	0.41
VV+HV	5.05	6.60	93.40	0.62	3.52	4.65	95.35	0.49
全极化	5.06	5.66	94.34	0.69	1.61	2.61	97.39	0.65

方法	FA/（%）	TE/（%)	OA/（%）	Kappa
传统方法+欧氏距离	2.43	3.18	96.82	0.63
传统方法+交叉熵	1.04	2.83	97.17	0.56
扩展方法+KL散度	1.14	2.35	97.65	0.67
扩展方法+JS散度	0.43	1.91	98.09	0.68
扩展方法+卡方距离	0.22	1.88	98.12	0.68
扩展方法+Bray Curtis距离	0.69	2.25	97.75	0.63
本文方法	0.38	1.67	98.33	0.73