The emerging threat for eco-sustainability has led to a breakthrough in satellite image analyses and such instantaneous monitoring of hazards could replenish the rejuvenation of natural ecosystem. Generally, the satellite images are huge dimensional data with numerous bands of specific details about the observed region. To apply immediate precautionary measures for environmental hazards and natural devastations, deploying a cloud-based intelligent web service for handling real time satellite image processing is inevitable. Therefore, the cloud implementation could afford integrated huge storage and parallel data processing tasks, the outcome of instantaneous satellite image processing relies with the effective data processing methods of less complexity. In this regard to address a major hazard of today which is drought monitoring, this paper focuses on developing an effective water segmentation method for such geospatial cloud web services. The Landsat 8 images of Sambhar lake region has been chosen for exploiting the water segmentation results. Most prevalent approaches from Spectral indices and unsupervised clustering such as normalized difference water index (NDWI), modified normalized difference water index, fuzzy C means, K-Means, Adaptive Regularized kernel fuzzy C means (ARKFCM) and simple linear iterative clustering-based superpixel segmentation (SLIC-SUPER) are compared, respectively. On comparative assessment using standard image quality assessment metrics, NDWI and ARKFCM outstands the rest with more accurate water body delineation. However, based on reduced computational complexity and instant localization, NDWI of spectral indexing approach clearly portray the significance of spectral influence in water body segmentation from satellite images. And it can be adapted as a persistent choice for instantaneous water body segmentation in a cloud-centered geospatial module.

中文翻译：

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用于卫星图像分析的光谱指数和基于软分类器的水体分割方法研究

生态可持续性的新威胁导致卫星图像分析的突破，这种对危害的即时监测可以补充自然生态系统的复兴。通常，卫星图像是巨大的维度数据，具有关于观察区域的许多特定细节带。为了立即对环境危害和自然灾害采取预防措施，部署基于云的智能网络服务来处理实时卫星图像处理是不可避免的。因此，云实现可以承担集成的海量存储和并行数据处理任务，瞬时卫星图像处理的结果依赖于复杂性较低的有效数据处理方法。在这方面，为了解决当今的主要危害，即干旱监测，本文的重点是为此类地理空间云网络服务开发一种有效的水分割方法。选择了 Sambhar 湖区的 Landsat 8 图像来利用水体分割结果。来自光谱指数和无监督聚类的最流行方法，例如归一化差异水指数 (NDWI)、修正归一化差异水指数、模糊 C 均值、K 均值、自适应正则化核模糊 C 均值 (ARKFCM) 和基于简单线性迭代聚类的超像素分割（SLIC-SUPER）分别进行比较。在使用标准图像质量评估指标的比较评估中，NDWI 和 ARKFCM 以更准确的水体描绘脱颖而出。然而，基于降低的计算复杂度和即时定位，光谱索引方法的 NDWI 清楚地描绘了光谱影响在卫星图像水体分割中的重要性。并且它可以适应作为以云为中心的地理空间模块中瞬时水体分割的持久选择。