Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach
Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach
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使用反射均质化方法从异质背景中提取基于 Landsat 8 OLI 图像的陆地水
DOI：
10.1016/j.rse.2015.10.005
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发表时间：
2015
影响因子：
13.5
通讯作者：
Duan Yuewei
中科院分区：
文献类型：
--
作者：
Yang Yuhao;Liu Yongxue;Zhou Minxi;Zhang Siyu;Zhan Wenfeng;Sun Chao;Duan Yuewei
地表沃茨是陆地生命的基本资源，但在全球范围内，它们并非不受自然和人为影响。一种准确、可靠的水体提取方法对于有效管理这些不可替代的资源至关重要。传统的方法往往受到限制的不确定性有关的粗分辨率和区域反射率的卫星图像的异质性。模糊聚类方法（FCM）考虑到当地的空间信息有一个证明的能力，以弥补这些限制。然而，该技术对原始卫星图像中的巨大假信号高度敏感。为此，本文设计了一种基于水指数（WI）和改进的FCM（WIMFCM）互补性的地表水提取方法，以提高水提取的精度，其基本原理是背景反射率偏差校正。应用程序进行了16个不同的测试站点从沿海到内陆沃茨，全面评估的可靠性WIMFCM使用Landsat-8业务陆地成像仪（OLI）的图像。结果表明，WIMFCM提高了水提取的准确性相比，其他方法的卡帕系数（KCs）和总分类误差（TE）。总体而言，WIMFCM的平均KC为0.94，其平均TE为11.39%，与原始WI（KC = 0.89，TE = 19.84%）和支持向量机（SVM）方法（KC = 0.89，TE = 22.39%）相比。此外，季节性分析表明，WIMFCM可以在全年保持一致的可靠性，这表明它具有与季节性水概率掩模相关的准确动态水监测的潜力。使用中分辨率成像光谱仪（MODIS）数据的其他测试显示，WIMFCM可以扩展到大规模的区域，以及用于近实时地表水体提取。研究结果为提高反射率非均匀环境下目标检测精度提供了一种新的方法。
Surface waters are fundamental resources for terrestrial life, yet they are not free of both natural and anthropogenic influences at global-scale. An accurate and robust method to extract water bodies is critical to effectively manage these irreplaceable resources. Conventional methods are frequently limited in terms of the uncertainty related to the coarse resolution and regional reflectance heterogeneity of satellite images. The fuzzy clustering method (FCM) considering local spatial information has a proven capability to compensate for these limitations. Nevertheless, this technique is highly sensitive to immense false signals in original satellite images. Therefore, a systematic surface water extraction method by taking advantage of the complementarity between a water index (WI) and a modified FCM (WIMFCM) was designed in this study to improve the water extraction accuracy, the rationale of which is a background reflectance bias correction. Applications were performed to sixteen test sites varying from coasts to inland waters to comprehensively evaluate the reliability of the WIMFCM using the Landsat-8 Operational Land Imager (OLI) images. Results showed the WIMFCM improved the accuracy of water extraction in comparison to alternative methods in terms of kappa coefficients (KCs) and total classification errors (TEs). Overall, the mean KC of the WIMFCM was 0.94 and its mean TE was 11.39%, compared with original WI (KC = 0.89, TE = 19.84%) and a support vector machine (SVM) method (KC = 0.89, TE = 22.39%). In addition, a seasonal analysis revealed the WIMFCM could maintain consistent reliability throughout the year, demonstrating its potential for accurate dynamic water monitoring associated with seasonal water probability mask. Additional tests using Moderate-Resolution Imaging Spectroradiometer (MODIS) data revealed the WIMFCM could be extended to large-scale regions, as well as be used in near-real time surface water body extraction. The findings of this study offer a new method to improve target detection accuracy under reflectance heterogeneous environments.