A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data
A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data
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DOI：
10.1016/j.rse.2013.10.008
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发表时间：
2014
影响因子：
13.5
通讯作者：
Jean-François Pekel;C. Vancutsem;L. Bastin;M. Clerici;E. Vanbogaert;E. Bartholomé;P. Defourny
中科院分区：
文献类型：
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作者：
Jean-François Pekel;C. Vancutsem;L. Bastin;M. Clerici;E. Vanbogaert;E. Bartholomé;P. Defourny
面对全球人口增长和供水分布不均的情况，更好地了解地表水资源的时空分布至关重要。遥感提供了对正在发生的过程的概括性看法，这涉及到水面的复杂性质，并允许对水生生态系统所受的压力进行评估。然而，从遥感数据确定水面的主要挑战是光谱特征在空间和时间上的高度可变性。在过去的10年里，只有少数操作方法被提出来绘制或监测大陆或全球范围的地表水，而且每种方法都显示出局限性。这项研究的目的是开发、演示和验证一种通用的多时相和多光谱图像分析方法是否足以自动探测水面，并在非洲大陆范围内对其进行近实时监测，作为实现全球范围覆盖的第一步。所提出的方法，基于转换的RGB颜色空间到HSV，提供了在大陆尺度上的动态信息。在非洲大陆范围内进行了两种不同的验证：i）算法验证检查了所提出的算法与人类对图像的解释一样有效的能力：它显示了96.6%的准确性，没有佣金错误。二）产品验证是通过使用高分辨率图像的独立数据集进行的：大陆永久水面产品显示出91.5%的准确度和很少的调试错误。所提出的方法的潜在应用已被确定和讨论。已经开发的方法是通用的：它可以应用于具有类似波段的传感器，具有良好的可靠性和最小的努力。此外，在非洲大陆范围内进行的这项实验表明，该方法对大范围的环境条件都是有效的。对其他大陆的进一步初步试验表明，拟议的方法也可以在全球范围内应用，而不会遇到太多困难。
In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objectives of this study are to develop, demonstrate and validate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near real-time at the African continental scale as a first step towards global scale coverage. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. Two different validations were done at the continental scale over Africa: i) The algorithm validation checked the ability of the proposed algorithm to perform as effectively as human interpretation of the image: it showed an accuracy of 96.6% and no commission errors. ii) The product validation was carried out by using an independent dataset derived from high resolution imagery: the continental permanent water surface product showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at the African continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties.