A novel slope adjusted water index (SAWI) is proposed to enhance the performance of the modified normalized difference water index (MNDWI) in mountainous areas, where water body and terrain shadow are prone to be confused, due to their similar spectral reflectance in the green and medium-wave infrared (MIR) bands. To overcome this problem, this method introduces a slope information derived from readily available ASTER GDEM-V2 data to the MNDWI, which provides a different scaling parameter to the MIR band for each pixel, such that the difference between terrain shadow and open water can be easily separated in mountainous areas. To validate the effectiveness of the proposed method, four typical sub-scenes clipped from the Operational Land Imager image with different terrain conditions were analyzed, and the results demonstrate that our method can not only possess the ability to relieve the effect of terrain shadow in rugged regions, but also enhance the subtle perception of narrow water body in plain areas, when compared with the MNDWI method. Comparisons with the normalized difference water index and the decision tree classification method were also implemented, and experimental results consistently show that the SAWI outperforms them for all the cases in terms of the used recall, precision and area under curve measurements.

中文翻译：

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带坡度信息的山区卫星影像水指数特征增强

提出了一种新的坡度调整水指数（SAWI），以提高修正归一化差异水指数（MNDWI）在山区的性能，山区水体和地形阴影容易混淆，因为它们在绿色的光谱反射率相似。和中波红外 (MIR) 波段。为了克服这个问题，该方法向 MNDWI 引入了从现成的 ASTER GDEM-V2 数据中导出的坡度信息，为每个像素的 MIR 波段提供了不同的缩放参数，从而可以区分地形阴影和开阔水域之间的差异。在山区容易分开。为了验证所提出方法的有效性，分析了从具有不同地形条件的 Operational Land Imager 图像中截取的四个典型子场景，结果表明，与MNDWI方法相比，我们的方法不仅可以减轻崎岖地区地形阴影的影响，而且可以增强平原地区狭窄水体的微妙感知。还实施了与归一化差异水指数和决策树分类方法的比较，实验结果一致表明，SAWI 在使用的​​召回率、精度和曲线下面积测量方面优于所有情况。