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UNSUPERVISED CLASSIFICATION OF HIGH-RESOLUTION REMOTE-SENSING IMAGES UNDER EDGE CONSTRAINTS

机译:边缘约束下的高分辨率遥感图像的无监督分类

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Classification is a crucial task in various remote sensing applications. While edge is one of the most important characteristics in the high-resolution remote-sensing images, which helps much for the improvement of classification accuracy. Therefore, in this paper, we propose an unsupervised classification method by incorporating edge information into a clustering procedure. Firstly, a consistency coefficient function, which indicates the similarity between edges obtained by clustering and by the edge detection methods, is defined to guarantee more accurate edges. Sequentially, a clustering procedure based on HMRFFCM is designed, in which the edge constraints are exploited by using the edge consistency. Experiments on synthetic and real remote sensing images have shown that the proposed methods can get more accurate classification results.
机译:分类是各种遥感应用中的重要任务。虽然边缘是高分辨率遥感图像中最重要的特征之一,这有助于提高分类准确性。因此,在本文中,我们通过将边缘信息结合到聚类过程中提出了无监督的分类方法。首先,定义了通过聚类和边缘检测方法获得的边缘之间的相似度的一致性系数函数,以保证更准确的边缘。顺序地,设计了基于HMRFFCM的聚类过程,其中通过使用边缘一致性来利用边缘约束。合成和真实遥感图像的实验表明,所提出的方法可以获得更准确的分类结果。

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