Commonly applied water indices such as the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) were originally conceived for medium spatial resolution remote sensing images. In recent decades, high spatial resolution imagery has shown considerable potential for deriving accurate land cover maps of urban environments. Applying traditional water indices directly on this type of data, however, leads to severe misclassifications as there are many materials in urban areas that are confused with water. Furthermore, threshold parameters must generally be fine-tuned to obtain optimal results. In this paper, we propose a new open surface water detection method for urbanized areas. We suggest using inequality constraints as well as physical magnitude constraints to identify water from urban scenes. Our experimental results on spectral libraries and real high spatial resolution remote sensing images demonstrate that by using a set of suggested fixed threshold values, the proposed method outperforms or obtains comparable results with algorithms based on traditional water indices that need to be fine-tuned to obtain optimal results. When applied to the ASTER and ECOSTRESS spectral libraries, our method identified 3677 out of 3695 non-water spectra. By contrast, NDWI and MNDWI only identified 2934 and 2918 spectra. Results on three real hyperspectral images demonstrated that the proposed method successfully identified normal water bodies, meso-eutrophic water bodies, and most of the muddy water bodies in the scenes with F-measure values of 0.91, 0.94 and 0.82 for the three scenes. For surface glint and hyper-eutrophic water, our method was not as effective as could be expected. We observed that the commonly used threshold value of 0 for NDWI and MNDWI results in greater levels of confusion, with F-measures of 0.83, 0.64 and 0.64 (NDWI) and 0.77, 0.63 and 0.59 (MNDWI). The proposed method also achieves higher precision than the untuned NDWI and MNDWI with the same recall values. Next to numerical performance, the proposed method is also physically justified, easy-to implement, and computationally efficient, which suggests that it has potential to be applied in large scale water detection problem.

中文翻译：

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使用高空间分辨率遥感图像在城市环境中进行开阔水域检测

常用的水指数，如归一化差值水指数（NDWI）和修正归一化差值水指数（MNDWI），最初是为中等空间分辨率遥感图像而设计的。近几十年来，高空间分辨率图像在获取精确的城市环境土地覆盖图方面显示出巨大的潜力。然而，将传统的水指数直接应用于此类数据会导致严重的错误分类，因为城市地区有许多与水相混淆的材料。此外，通常必须微调阈值参数以获得最佳结果。在本文中，我们提出了一种新的城市化地区开放地表水检测方法。我们建议使用不平等约束以及物理量级约束来识别城市场景中的水。我们在光谱库和真实高空间分辨率遥感图像上的实验结果表明，通过使用一组建议的固定阈值，所提出的方法优于基于需要微调以获得的传统水指数的算法或获得可比的结果最佳结果。当应用于 ASTER 和 ECOSTRESS 光谱库时，我们的方法识别了 3695 个非水光谱中的 3677 个。相比之下，NDWI 和 MNDWI 只能识别 2934 和 2918 个光谱。在三幅真实高光谱图像上的结果表明，该方法成功识别了场景中的正常水体、中富营养水体和大部分泥水体，三个场景的 F-measure 值分别为 0.91、0.94 和 0.82。对于表面闪烁和超富营养化的水，我们的方法没有预期的那么有效。我们观察到，NDWI 和 MNDWI 的常用阈值 0 会导致更大程度的混淆，F 值分别为 0.83、0.64 和 0.64 (NDWI) 以及 0.77、0.63 和 0.59 (MNDWI)。所提出的方法还比具有相同召回值的未调谐 NDWI 和 MNDWI 实现了更高的精度。除了数值性能外，所提出的方法在物理上也是合理的，易于实现且计算效率高，这表明它具有应用于大规模水检测问题的潜力。所提出的方法还比具有相同召回值的未调谐 NDWI 和 MNDWI 实现了更高的精度。除了数值性能外，所提出的方法在物理上也是合理的，易于实现且计算效率高，这表明它具有应用于大规模水检测问题的潜力。所提出的方法还比具有相同召回值的未调谐 NDWI 和 MNDWI 实现了更高的精度。除了数值性能外，所提出的方法在物理上也是合理的，易于实现且计算效率高，这表明它具有应用于大规模水检测问题的潜力。