Abstract. Spatial data mining discovers patterns and knowledge in spatial data. The geospatial data analysis plays a decisive role in framing essential policies related to the environment at the national as well as global level. It helps in the prediction of weather, studying the impact of climate change, the effect of global warming, forest fire, deforestation, and other causes of changes in nature. Clustering is a process of assigning similar objects to a group and dissimilar objects to different groups. We proposed a clustering algorithm, spatial clustering using neighborhood (SCN), in which similar neighboring pixels groups together to form a cluster. It is a two-step process: first, identification of clusters of similar neighboring pixels and second, merging of spatially separated similar clusters. Experiments with the implementation of the proposed approach have been carried out on three Landsat 5 thematic mapper images of Delhi, Chilika lake, and Kolkata region. In terms of normalized cluster quality parameters, SCN produced better clusters than other discussed algorithms in the literature.

中文翻译：

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使用邻域对多光谱图像进行空间聚类

摘要。空间数据挖掘发现空间数据中的模式和知识。地理空间数据分析在制定与国家和全球层面的环境相关的基本政策方面起着决定性的作用。它有助于预测天气、研究气候变化的影响、全球变暖的影响、森林火灾、森林砍伐和其他自然变化的原因。聚类是将相似的对象分配给一个组并将不同的对象分配给不同的组的过程。我们提出了一种聚类算法，使用邻域（SCN）进行空间聚类，其中相似的相邻像素组合在一起形成一个聚类。这是一个两步过程：首先，识别相似相邻像素的簇，然后合并空间分离的相似簇。已在德里、奇利卡湖和加尔各答地区的三张 Landsat 5 专题地图图像上进行了实施所提议方法的实验。在归一化聚类质量参数方面，SCN 产生了比文献中其他讨论的算法更好的聚类。