Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (FCM) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise” of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional FCM algorithm, this paper proposes an improved FCM algorithm. This algorithm replaces the Euclidean distance of the traditional FCM algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images of the Fuxian Lake and Xingyun Lake basins during normal, wet, and dry periods as the data source. Under the same conditions, the clustering accuracy was compared with the traditional FCM algorithm, improved FCM algorithm, K-means clustering method and iterative self-organizing data analysis (ISODATA) clustering method. The experimental results show that the improved FCM algorithm has a higher water extraction accuracy than the traditional FCM algorithm, K-means clustering method and ISODATA clustering method. The kappa coefficient and overall accuracy (OA) of the improved FCM algorithm can be increased by 5.56%–9.45% and 2.66%–5.32%, respectively, and the omission error and commission error can be reduced by 1.72%–4.55% and 12.14%–22.10%, respectively. When the improved FCM algorithm is used, the extraction accuracy is higher for plateau deep lakes than for plateau shallow lakes, and the extraction effect for lakes with poor water environments is more significant than that of other methods. The improved FCM algorithm better maintains the integrity of the water boundary and overcomes the influence of a certain number of mountain shadows and urban building pixels on the clustering results.

中文翻译：

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基于改进FCM算法的高原湖泊水体提取

高原体湖水变化是全球生态系统变化的重要指标，及时准确掌握这一变化信息可为相关政策的制定提供科学参考。传统的模糊C均值聚类（FCM）算法考虑到地物像素分类的模糊性，但没有考虑相邻像素丰富的光谱信息，对遥感图像的“背景噪声”非常敏感。 ，导致水提取精度低。针对传统FCM算法的不足，提出一种改进的FCM算法。该算法将传统FCM算法的欧氏距离替换为马氏距离和光谱角匹配（SAM）的组合，充分考虑了相邻像素的光谱信息，提高了聚类精度。研究选取抚仙湖和星云湖流域正常、湿润和干旱时期的哨兵2号图像作为数据源。在相同条件下，将聚类精度与传统FCM算法、改进FCM算法、K-means聚类方法和迭代自组织数据分析(ISODATA)聚类方法进行比较。实验结果表明，改进的FCM算法比传统的FCM算法、K-means聚类方法和ISODATA聚类方法具有更高的水提取精度。改进后的 FCM 算法的 kappa 系数和整体精度（OA）分别提高了 5.56%–9.45% 和 2.66%–5.32%，遗漏误差和委托误差分别降低了 1.72%–4.55% 和 12.14 %–22.10%，分别。采用改进的FCM算法时，高原深湖提取精度高于高原浅湖，对水环境较差的湖泊提取效果比其他方法更显着。改进的FCM算法更好地保持了水边界的完整性，克服了一定数量的山体阴影和城市建筑像素对聚类结果的影响。遗漏误差和委托误差可分别降低1.72%~4.55%和12.14%~22.10%。采用改进的FCM算法时，高原深湖提取精度高于高原浅湖，对水环境较差的湖泊提取效果比其他方法更显着。改进的FCM算法更好地保持了水边界的完整性，克服了一定数量的山体阴影和城市建筑像素对聚类结果的影响。遗漏误差和委托误差可分别降低1.72%~4.55%和12.14%~22.10%。采用改进的FCM算法时，高原深湖提取精度高于高原浅湖，对水环境较差的湖泊提取效果比其他方法更显着。改进的FCM算法更好地保持了水边界的完整性，克服了一定数量的山体阴影和城市建筑像素对聚类结果的影响。