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Segmentation algorithm of high resolution remote sensing images based on LBP and statistical region merging

机译:基于LBP和统计区域合并的高分辨率遥感影像分割算法

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Remote sensing image segmentation is the basis of object-oriented classification of remote sensing images. It is important for the application of remote sensing images. High-resolution remote sensing images contain rich spatial texture information. SRM is an efficient image segmentation algorithm. This paper presents a segmentation algorithm to take full advantage of the high-resolution remote sensing image texture information based on LBP and SRM, in the process of merging, according to the characteristics of regions, select the appropriate method to merge. It works well in the segmentation of high-resolution remote sensing images.
机译:遥感图像分割是遥感图像面向对象分类的基础。这对于遥感图像的应用很重要。高分辨率遥感影像包含丰富的空间纹理信息。 SRM是一种有效的图像分割算法。提出一种分割算法,充分利用基于LBP和SRM的高分辨率遥感影像纹理信息,在合并过程中,根据区域特征,选择合适的合并方法。它在高分辨率遥感影像的分割中效果很好。

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