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首页> 外文期刊>Computer Modeling in Engineering & Sciences >Threshold-Based Adaptive Gaussian Mixture Model Integration (TA-GMMI) Algorithm for Mapping Snow Cover in Mountainous Terrain
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Threshold-Based Adaptive Gaussian Mixture Model Integration (TA-GMMI) Algorithm for Mapping Snow Cover in Mountainous Terrain

机译：基于阈值的自适应高斯混合模型集成（TA-GMMI）山区地形中雪覆盖的算法

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摘要

Snow cover is an important parameter in the fields of computer modeling, engineering technology and energy development. With the extensive growth of novel hardware and software compositions creating smart, cyber physical systems' (CPS) efficient end-to-end workflows. In order to provide accurate snow detection results for the CPS's terminal, this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model (GMM) for the FY-4A satellite data. At present, most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum, which is based on the normalized difference snow index (NDSI) with thresholds in different wavebands. These algorithms require a large amount of manually labeled data for statistical analysis to obtain the appropriate thresholds for the study area. Consideration must be given to both the high and low elevations in the study area. It is difficult to extract all snow by a fixed threshold in mountainous and rugged terrains. In this research, we avoid relying on a manual analysis for different elevations. Therefore, an algorithm based on the GMM is proposed, integrating the threshold-based algorithm and the GMM. First, the threshold-based algorithm with transferred thresholds from other satellites' analysis results are used to coarsely classify the surface objects. These results are then used to initialize the parameters of the GMM. Finally, the parameters of that model are updated by an expectation-maximum (EM) iteration algorithm, and the final results are outputted when the iterative conditions end. The results show that this algorithm can adjust itself to mountainous terrain with different elevations, and exhibits a better performance than the threshold-based algorithm. Compared with orbit satellites' snow products, the accuracy of the algorithm used for FY-4A is improved by nearly 2%, and the snow detection rate is increased by nearly 6%. Moreover, compared with microwave sensors' snow products, the accuracy is increased by nearly 3%. The validation results show that the proposed algorithm can be adapted to a complex terrain environment in mountainous areas and exhibits good performance under a transferred threshold without manually assigned labels.
机译：雪覆盖是计算机建模，工程技术和能源发展领域的重要参数。随着新型硬件和软件组合的广泛增长，创建智能网络物理系统（CPS）有效的端到端工作流程。为了为CPS终端提供准确的雪检测结果，本文提出了一种基于无监督的高斯混合模型（GMM）的雪覆探测算法，用于FY-4A卫星数据。目前，大多数雪覆盖检测算法主要利用光谱的特性，其基于具有不同波段的阈值的归一化差异雪指数（NDSI）。这些算法需要大量的手动标记数据进行统计分析，以获得研究区域的适当阈值。必须考虑研究区域中的高高和低升高。在山区和坚固的地形中，难以通过固定的阈值提取所有雪。在这项研究中，我们避免依靠手动分析不同的海拔。因此，提出了一种基于GMM的算法，集成了基于阈值的算法和GMM。首先，使用来自其他卫星分析结果的转移阈值的基于阈值的算法用于粗略分类表面对象。然后使用这些结果来初始化GMM的参数。最后，通过期望 - 最大（EM）迭代算法更新该模型的参数，并且在迭代条件结束时输出最终结果。结果表明，该算法可以将自身调整为具有不同高度的山区地形，并且表现出比基于阈值的算法更好的性能。与轨道卫星的雪地产品相比，用于FY-4A的算法的准确性提高了近2％，雪检测速率提高了近6％。此外，与微波传感器的雪产品相比，精度增加了近3％。验证结果表明，该算法可以适应山区的复杂地形环境，在没有手动分配标签的情况下在转移的阈值下表现出良好的性能。

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