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

被引：0

作者：

Zhao, Jinqi ^{[1
,2
]}

Li, Yuxuan ^{[1
]}

Liu, Zirong ^{[1
,2
]}

An, Qing ^{[3
]}

Song, Shiyu ^{[1
]}

Niu, Yufen ^{[4
]}

机构：

[1] School of Environment and Spatial informatics, China University of Mining and Technology, Xuzhou

[2] Key Laboratory of National Geographie Census Monitoring, Ministry of Natural Resources, Wuhan

[3] Artificial Intelligence School, Wuchang University of Technology, Wuhan

[4] School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2024年 / 53卷 / 12期

基金：

中国国家自然科学基金;

关键词：

change detection; cross entropy; flood; improved K-means; temporal-spatial-polarized;

D O I：

10.11947/j.AGCS.2024.20230355

中图分类号：

学科分类号：

摘要：

Thanks to its ability for all-weather and all-day Observation, synthetic aperture radar (SAR) enables flood monito-ring in harsh environments. Currently, flood change detection methods are easily affected by the changes of other ground objeets and designed inadequaey 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 construet temporal-spatial-polarized SAR data. Furthermore, the improved K-means clustering approach for construeted data reduces aecumulated errors from different temporal clustering processing. In addition, considering the distribution characteristics of temporal-spatial-polarized SAR data, Gross Entropy is designed to optimize the similarity measurement function to accurately distinguish water body changes caused by flooding. Finally, multi-temporal fully Polarimetrie Radarsat-2 data from Wuhan and dual Polarimetrie 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 aecuraey (OA), and Kap-pa 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 fea-tures 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. © 2024 SinoMaps Press. All rights reserved.

引用

页码：2375 / 2390

页数：15

共 43 条

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