Detecting and monitoring surface water has received much attention in recent decades. Surface water is one of the most critical water resources for both human and ecological systems. Remote sensing technology has made it possible to have accurate and frequent updates of surface water. We propose a remote sensing multisensor fusion system using optical data including Landsat-8 and Sentinel-2 and RADAR data including Sentinel-1 for water body extraction. Using a data fusion approach, the spatial resolution of multispectral images increased from 30 to 10 m, and the spectral information of Landsat-8 and Sentinel-2 were preserved. Then, all features extracted from the high spatial resolution images, water index maps, and Sentinel-1 dataset were combined as a stacked feature space. The new data were subsequently classified by support vector machine, neural network, and random forest. Finally, all classifiers’ outputs were integrated using a weighted majority voting decision fusion strategy, which significantly improved surface water extraction. Our study illustrates the ability of remote sensing multisensory fusion, water indices, and decision fusion for water body extraction.

中文翻译：

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Sentinel-1和Sentinel-2数据融合系统用于地表水提取

在最近的几十年中，地表水的检测和监测受到了广泛的关注。地表水是人类和生态系统最重要的水资源之一。遥感技术使准确，频繁地更新地表水成为可能。我们提出了一种遥感多传感器融合系统，该系统利用光学数据（包括Landsat-8和Sentinel-2）和RADAR数据（包括Sentinel-1）进行水体提取。使用数据融合方法，多光谱图像的空间分辨率从30 m增加到10 m，并保留了Landsat-8和Sentinel-2的光谱信息。然后，将从高空间分辨率图像，水索引图和Sentinel-1数据集中提取的所有特征组合为一个堆叠的特征空间。随后通过支持向量机对新数据进行分类，神经网络和随机森林。最后，所有分类器的输出均采用加权多数投票决策融合策略进行整合，从而显着改善了地表水的提取。我们的研究说明了遥感多传感器融合，水指数和决策融合在水体提取中的能力。