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PRISI: A novel piecewise radar impervious surface index for urban monitoring using Sentinel-1 data
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-24 , DOI: 10.1016/j.jag.2022.103033
Yulin Ding, Qing Ding, Jie Yang, Zhenfeng Shao, Xiao Huang

The impervious surface (IS) is an important symbol of the urban ecological environment and urbanization process. An accurate, simple, and effective IS extraction method is crucial to environmental and social-economic research. The widely used optical data are frequently disturbed by cloud cover in low latitude and humid areas, bringing great difficulties to remote sensing monitoring of IS. Thus, it is necessary to fully explore the capability of IS extraction relying on SAR data sources alone. However, due to the diversity of IS materials and low spectral consistency, IS extraction in an accurate manner using the backscatter characteristics remains to be challenging. Therefore, we innovatively analyze the complementary effect between backscatter and interferometric coherence from the perspective of IS heterogeneity. Further, we propose a Sentinel-1-based Piecewise Radar Impervious Surface Index (PRISI) that takes into account both the high scattering characteristics and the physical stability of IS. We demonstrate that the proposed adaptive index PRISI-A based on the Gaussian mixture model (GMM), Log-Gaussian mixture model (LGMM) and Expectation maximum (EM) algorithm leads to superior performance. Moreover, we provide a non-adaptive index PRISI-D, which adopts empirical parameter settings from trial and error. The computational-friendly PRISI-D yields comparable results compared to PRISI-A. The experimental results show that the IS extraction overall accuracy of PRISI-D and PRISI-A achieve 87.50 % and 88.11 %, which consistently outperform the previous optical indexes by 6.2–11.7 % and the existing radar indexes by 5.2–21.4 % on average. The results reveal that PRISI owns robust regional universality and seasonal suitability, which significantly improves the capability of IS extraction relying on SAR data sources alone.



中文翻译:

PRISI:使用 Sentinel-1 数据进行城市监测的新型分段雷达不透水表面指数

不透水面(IS)是城市生态环境和城市化进程的重要标志。准确、简单、有效的 IS 提取方法对于环境和社会经济研究至关重要。 广泛使用的光学数据在低纬度和潮湿地区经常受到云层的干扰,给IS的遥感监测带来了很大的困难。因此,有必要充分挖掘仅依赖 SAR 数据源的 IS 提取能力。然而,由于 IS 材料的多样性和低光谱一致性,使用反向散射特性以准确方式提取 IS 仍然具有挑战性。因此,我们从IS异质性的角度创新地分析了后向散射与干涉相干的互补效应。此外,我们提出了一种基于 Sentinel-1 的分段雷达不透水表面指数 (PRISI),该指数同时考虑了 IS 的高散射特性和物理稳定性。我们证明了基于高斯混合模型(GMM)、对数高斯混合模型(LGMM)和期望最大值(EM)算法提出的自适应索引PRISI-A具有优越的性能。此外,我们提供了一个非自适应指数 PRISI-D,它采用了反复试验的经验参数设置。与 PRISI-A 相比,计算友好的 PRISI-D 产生了可比较的结果。实验结果表明,PRISI-D和PRISI-A的IS提取整体精度分别达到87.50%和88.11%,平均优于以往光学指标6.2-11.7%和现有雷达指标5.2-21.4%。它采用试验和错误的经验参数设置。与 PRISI-A 相比,计算友好的 PRISI-D 产生了可比较的结果。实验结果表明,PRISI-D和PRISI-A的IS提取整体精度分别达到87.50%和88.11%,平均优于以往光学指标6.2-11.7%和现有雷达指标5.2-21.4%。它采用试验和错误的经验参数设置。与 PRISI-A 相比,计算友好的 PRISI-D 产生了可比较的结果。实验结果表明,PRISI-D和PRISI-A的IS提取整体精度分别达到87.50%和88.11%,平均优于以往光学指标6.2-11.7%和现有雷达指标5.2-21.4%。 结果表明,PRISI具有较强的区域普适性和季节适用性,显着提高了仅依靠SAR数据源进行IS提取的能力。

更新日期:2022-09-25
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