高分辨率遥感图像均值调整法分割技术研究

The Method of Segmenting High-resolution Remote Sensing Images Based on Mean-shift

遥感图像分割是将以像元为基础的图像转化为以对象为基础的过程,因此,遥感图像分割是遥感图像高级分析的基础.对于中、低分辨率遥感图像来说,单个像元的面积较大,且混合像元现象严重,图像分割会产生较大的误差.高分辨率遥感图像单个像元的面积较小,分割后图像上单个对象内含若干像元,便于分析和提取信息.以高分辨率遥感图像Q u ickB ird和IKONO S为研究对象,采用均值调整法对图像进行分割和精度检验.结果表明:采用均值调整法进行高分辨率遥感图像分割具有较好的效果,图像分割的速度和精度均较高.

Image segmentation is a process of changing pixel-based images into object-based ones; so in remote sensing, image segmentation is the base of high-level image analysis. Image segmentation brings errors in low or medium-resolution remote sensing images because a single pixel covers a larger area and there are a lot of mixed pixels. On the contrary, in a high-resolution remote sensing image, a pixel covers a smaller area and there are less mixed pixels; so a single object on a segmented image includes a number of pixels, and thus makes it possible to analyze and extract information. In this paper, QuickBird image was segmented by mean-shift and its accuracy was checked. The result shows that mean-shift is a good method of segmentation to high-resolution remote sensing images ; both the velocity and accuracy of segmentation are satisfied.