基于迭代阈值分割的星载SAR洪水区域快速提取

doi:10.12305/j.issn.1001-506X.2022.09.08

摘要/Abstract

摘要：

基于星载合成孔径雷达(synthetic aperture radar, SAR)的洪水区域提取可对洪灾信息进行高效提取。然而, 传统提取方法往往时间复杂度较高, 严重影响了洪灾区域获取的时效性。基于改进迭代阈值分割原理, 本文提出了一种星载SAR图像洪水区域快速提取方法。首先, 对预处理后的SAR图像进行高斯拟合再抽样, 抑制SAR图像直方图异常点的影响。其次, 利用迭代阈值算法进行水体提取, 并基于形态学滤波对噪声进行抑制。最后, 对已识别的水体区域开展变化检测, 实现洪灾区域的提取。基于2020年7月鄱阳湖流域特大洪灾前后的哨兵-1 SAR图像, 本文开展了洪水区域提取对比试验。试验结果表明, 该方法可在保证洪水区域提取精度的同时, 显著提升处理效率。

关键词: 合成孔径雷达, 迭代阈值分割, 变化检测, 洪水区域提取

Abstract:

Flood area extraction method based on spaceborne synthetic aperture radar (SAR) can extract flood area with high precision. However, traditional flood area extraction methods are time consuming, which will seriously affect the time effectiveness of flood area extraction. In this paper, an improved iterative threshold segmentation method for fast extraction of flood area is proposed, using spaceborne SAR images. Firstly, the preprocessed SAR image is resampled by Gaussian fitting to suppress the influence of abnormal points in the histogram of SAR image. Then, the water body is extracted by the iterative threshold segmentation method, and the noise suppression is performed using the morphological filtering. Finally, change detection is carried out to obtain the flood area, based on the extracted water body. Based on the Sentinel-1 SAR images of Poyang Lake before and after the flood in July 2020, a comparative experiment of flood area extraction is carried out in this paper. Experimental results shows that the method can extract the flood area quickly with high accuracy.

Key words: synthetic aperture radar (SAR), iterative threshold segmentation, change detection, flood area extraction

中图分类号:

TP751.1

苗添, 曾虹程, 王贺, 陈杰. 基于迭代阈值分割的星载SAR洪水区域快速提取[J]. 系统工程与电子技术, 2022, 44(9): 2760-2768.

Tian MIAO, Hongcheng ZENG, He WANG, Jie CHEN. A fast extraction method of flood areas based on iterative threshold segmentation using spaceborne SAR data[J]. Systems Engineering and Electronics, 2022, 44(9): 2760-2768.

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序号	获取时间	中心坐标	分辨率/m
1	2019/07/20	(116.05°E, 29.01°N)	5×20
2	2020/03/16	(116.05°E, 29.01°N)	5×20
3	2020/07/14	(116.05°E, 29.01°N)	5×20

名称	指标	序号
名称	指标	1	2	3	4	5	6
数据1拟合函数参数	a_k	5.851×10⁴	0	-1 470	3.215×10⁵	7.663×10⁴	1.187×10⁵
	b_k	170.8	174.7	151.2	167.8	164	63.86
	c_k	12.14	0.048 12	5.611	20.52	48.07	24.46
数据2拟合函数参数	a_k	5.465×10⁵	-2.266×10⁵	-5.991×10⁴	1.478×10⁵	8.147×10⁴	-
	b_k	155.3	160.8	110.4	133.1	49.81	-
	c_k	18.1	15.13	28.9	52.75	17.08	-
数据3拟合函数参数	a_k	2.698×10⁵	-1.69×10⁴	3.999×10⁴	-9.646×10⁴	1.527×10⁵	1.525×10⁵
	b_k	168.3	25.84	189.0	115.8	156.8	82.12
	c_k	18.28	12.82	10.84	41.37	43.06	39.89

提取方式	阈值/dB	水体占比/%	准确率/%	虚警率/%	漏警率/%	计算时间/s
迭代阈值分割算法	-16.18	23.31	87.0	6.0	7.0	1.045
Otsu阈值分割算法	-16.32	23.17	87.6	5.7	6.7	10.016
K-Means非监督性学习	-	23.27	84.3	9.0	6.7	78.153
EM算法	-	22.86	88.7	5.0	6.3	88.897

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