School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
School of Humanities, Universiti Sains Malaysia, 11800 George Town, Penang, Malaysia.
Sci Total Environ. 2021 Nov 10;794:148388. doi: 10.1016/j.scitotenv.2021.148388. Epub 2021 Jun 25.
The SAR has the ability of all-weather and all-time data acquisition, it can penetrate the cloud and is not affected by extreme weather conditions, and the acquired images have better contrast and rich texture information. This paper aims to investigate the use of an object-oriented classification approach for flood information monitoring in floodplains using backscattering coefficients and interferometric coherence of Sentinel-1 data under time series. Firstly, the backscattering characteristics and interference coherence variation characteristics of SAR time series are used to analyze whether the flood disaster information can be accurately reflected and provide the basis for selecting input classification characteristics of subsequent SAR images. Subsequently, the contribution rate index of the RF model is used to calculate the importance of each index in time series to convert the selected large number of classification features into low dimensional feature space to improve the classification accuracy and reduce the data redundancy. Finally, the SAR image features in each period after multi-scale segmentation and feature selection are jointly used as the input features of RF classification to extract and segment the water in the study area to monitor floods' spatial distribution and dynamic characteristics. The results showed that the various attributes of backscatter coefficients and interferometric coherence under time series could accurately correspond with the actual flood risk, and the combined use of backscattering coefficient and interferometric coherence for flood extraction can significantly improve the accuracy of flood information extraction. Overall, the object-based random forest method using the backscattering coefficient and interference coherence of Sentinel-1 time series for flood extraction advances our understanding of flooding's temporal and spatial dynamics, essential for the timely adoption of adaptation and mitigation strategies for loss reduction.
SAR 具有全天候、全时段数据获取能力，能够穿透云层，不受极端天气条件的影响，获取的图像具有更好的对比度和丰富的纹理信息。本文旨在研究利用基于对象的分类方法，基于 Sentinel-1 数据的后向散射系数和干涉相干性时间序列，监测洪泛区的洪水信息。首先，利用 SAR 时间序列的后向散射特征和干涉相干性变化特征，分析洪水灾害信息是否能够准确反映，并为后续 SAR 图像输入分类特征的选择提供依据。随后，利用 RF 模型的贡献率指标，计算时间序列中各指标的重要性，将选择的大量分类特征转换为低维特征空间，以提高分类精度，减少数据冗余。最后，将多尺度分割和特征选择后的 SAR 图像特征在每个时段联合作为 RF 分类的输入特征，提取和分割研究区域的水体，监测洪水的空间分布和动态特征。结果表明，时间序列中后向散射系数和干涉相干性的各种属性能够准确对应实际的洪水风险，后向散射系数和干涉相干性的联合使用可以显著提高洪水信息提取的精度。总的来说，使用 Sentinel-1 时间序列的后向散射系数和干涉相干性进行基于对象的随机森林洪水提取方法，提高了我们对洪水时空动态的认识，对于及时采取适应和缓解策略减少损失至关重要。
