一种改进的变化检测方法及其在洪水监测中的应用

冷英; 李宁

doi:10.12000/JR16139

一种改进的变化检测方法及其在洪水监测中的应用

DOI: 10.12000/JR16139

冷英^①,②,,
李宁^①, ,

①.
中国科学院电子学研究所北京 100190
②.
中国科学院大学北京 100190

基金项目: 国家自然科学基金优秀青年基金(61422113)

详细信息

作者简介:
冷　英(1987–)，女，辽宁人，博士研究生，研究方向为SAR图像信息提取。E-mail: sarallyy@126.com

李宁(1987–)，男，安徽人，博士，中科院电子学研究所助理研究员，研究方向为多模式合成孔径雷达成像及其应用技术研究。E-mail: lining_nuaa@163.com

通讯作者:
李宁 lining_nuaa@163.com

中图分类号: TP753
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- 被引次数: 0
出版历程
- 收稿日期: 2016-12-05
- 修回日期: 2017-02-16
- 网络出版日期: 2017-04-28

Improved Change Detection Method for Flood Monitoring

Leng Ying^{①,②
,},
Li Ning^{①
, ,}

①.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
②.
University of Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation for Excellent Young Scholars (61422113)

摘要

摘要: 针对多时相合成孔径雷达(Synthetic Aperture Radar, SAR)图像的变化检测，该文提出一种改进的混合变化检测方法来提高检测精度。该方法首先采用基于像素级的变化检测方法提取初始变化区域，并以此估计初始聚类中心；然后采用模糊聚类(FCM)将变化前后SAR图像分为3类，即水体区域、背景区域、过渡区域；接着采用最近距离聚类(NNC)将过渡区域像素进一步划分为水体和背景两部分，合并所有水体像素，实现洪水区域的提取。最后得到的洪水区域差异图即为最终的变化检测结果。该文采用Sentinel-1A获取的淮河与鄱阳湖水域数据进行算法验证，实验表明，该文方法的检测率较高，且总体误差较低。
- 变化检测 /
- SAR /
- 模糊聚类 /
- 混合变化检测 /
- Sentinel-1A
Abstract: An improved Hybrid Change Detection (HCD) method is proposed for multi-temporal Synthetic Aperture Radar (SAR) images. Firstly, a Pixel-Based Change Detection (PBCD) method is used to extract the initial change area, and the initial cluster center is estimated based on its results. Then, Fuzzy Clustering Method (FCM) is used to get three clusters, which including water, background, and the intermediate area. The Nearest Neighbor Clustering (NNC) is adopted as the second-level clustering to divide the pixels belonging to the intermediate area into water and background respectively, afterwards merge all pixels belonging to water. Finally, the difference map of flood region in the time series images is calculated to get the final change detection result. The algorithm is validated by the Sentinel-1A data obtained from Huaihe River and Poyang Lake. The results show that our proposed method can achieve better correctness and has lower total error compared to other methods.
- Change detection /
- Synthetic Aperture Radar (SAR) /
- Fuzzy Clustering Method (FCM) /
- Hybrid Change Detection (HCD) /
- Sentinel-1A

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图 1 算法流程

Figure 1. Workflow of the proposed algorithm

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图 2 实验场景1：淮河流域

Figure 2. Experimental scene 1 covering Huaihe areas acquired through Sentinel-1A

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图 3 实验场景2：鄱阳湖水域

Figure 3. Experimental scene 2 covering Poyang Lake areas acquired through Sentinel-1A

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图 4 待分析的图像切片

Figure 4. Slice under analysis

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图 5 PCA融合对比分析

Figure 5. Image fusion with PCA analysis

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图 6 PBCD变化检测分析

Figure 6. Analysis of PBCD approach

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图 7 A区域变化检测结果对比分析

Figure 7. Change detection of A region with different approaches

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图 8 B区域变化检测结果对比分析

Figure 8. Change detection of B region with different approaches

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表 1 Sentinel-1A数据参数(干涉宽测绘带模式)

Table 1. Parameters of Sentinel-1A product (IW)

参数	数值
成像波段	C
载频	5.4 GHZ
幅宽	250 km
入射角	38.9°
极化	VV HV
地距分辨率	20×22 m

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表 2 变化检测精度对比分析

Table 2. Quantitative evalutaions and comparison of change detection results

方法	检测率(%)		虚警率(%)		总误差率(%)		Kappa
方法	A	B	A	B	A	B	A	B
本文方法	97.60	87.32	1.93	2.71	2.08	3.83	0.96	0.86
文献[8]方法	94.34	79.40	1.00	3.97	2.44	5.84	0.92	0.76
文献[21]方法	93.40	73.77	0.65	1.33	2.53	4.14	0.91	0.71
Try and error	93.33	69.15	0.53	1.12	2.47	4.47	0.90	0.70
FCM	95.84	86.26	2.36	3.17	2.93	4.37	0.94	0.84

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