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CLUSTERING METHOD BASED ON FUSION OF MULTI-SPECTRAL IMAGES

机译:基于多光谱图像融合的聚类方法

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Image segmentation is a very common problem in image processing field with many applications, which results in different research branches such as change detection, object tracking, segmentation of maps, multi-temporal filtering, etc. This paper focuses on clustering problem based on fusion of multi-spectral images/satellite photos, which belongs to the remote sensing field. The steps are as follows: First, the projection from n-D to 1-D is used as the fusion process of multi'spectral images; then based on the fusion result, K-means method is chosen to cluster pixels into different classes, which should represent different regions. For the demand of our project FLOCODS, river regions are paid to high attention. The results of the tests proved our idea feasible and good when compared with another software in this field, Multispec.
机译:图像分割是图像处理领域中一个非常普遍的问题,其应用广泛,导致不同的研究领域,如变化检测,目标跟踪,地图分割,多时间滤波等。多光谱图像/卫星照片,属于遥感领域。步骤如下:首先,使用从n-D到1-D的投影作为多光谱图像的融合过程。然后根据融合结果,选择K-means方法将像素聚类为不同的类别,这些类别应代表不同的区域。对于我们的FLOCODS项目的需求,河区受到了高度重视。测试结果证明,与该领域的另一软件Multispec相比,我们的想法可行且良好。

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