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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Application of a Multiseed-Based Clustering Technique for Automatic Satellite Image Segmentation
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Application of a Multiseed-Based Clustering Technique for Automatic Satellite Image Segmentation

机译：基于多种子的聚类技术在卫星图像自动分割中的应用

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The problem of classifying an image into different homogeneous regions is viewed as a task of clustering the pixels in the intensity space. In this letter, a newly developed genetic clustering technique is used for automatically segmenting remote sensing satellite images. Each cluster is divided into several small hyperspherical subclusters, and the centers of all these small subclusters are encoded in a chromosome to represent the whole clustering. For assigning points to different clusters, these local subclusters are considered individually. For the purpose of objective function evaluation, these subclusters are merged appropriately to form a variable number of global clusters. A newly proposed point-symmetry-distance-based cluster validity index, Sym index, is used as a measure of the validity of the corresponding segment. The effectiveness of the proposed technique compared to a fuzzy C-means clustering technique, a recently proposed GAPS clustering with Sym-index-based method, and a subtractive clustering technique is demonstrated in identifying different land cover regions from two numeric image data sets and a remote sensing image of a part of the city of Kolkata.
机译：将图像分类到不同的均匀区域的问题被视为将强度空间中的像素聚类的任务。在这封信中，新开发的遗传聚类技术用于自动分割遥感卫星图像。每个簇被划分为几个小的超球形子簇，所有这些小的子簇的中心被编码在一条染色体中，以代表整个簇。为了将点分配给不同的群集，这些本地子群集被单独考虑。为了进行目标函数评估，将这些子集群适当合并以形成可变数量的全局集群。一种新提出的基于点对称距离的聚类有效性指数Sym指数用作相应段有效性的度量。与模糊C均值聚类技术，最近提出的基于Sym-index的GAPS聚类以及减法聚类技术相比，该技术在从两个数字图像数据集和两个数字图像数据集识别不同的土地覆盖区域中的有效性得到了证明。加尔各答市一部分的遥感图像。

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