Satellite image interpretation using Genetically Optimized Hard C means

B. Sowmya
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Abstract

This paper explains the task of interpreting any given satellite image by Genetically Optimized Hard C means(GOHCM). GOHCM has been used to segment the satellite image. Image segmentation is the process of dividing pixels into homogeneous classes or clusters so that items in the same cluster are as similar as possible and items in different cluster are as dissimilar as possible. The most basic attribute for segmentation is image luminance amplitude for a monochrome image and color components for a color image. Since there are more than 16 million colours available in any given colour image, it is difficult to analyze the image on its entire colour. Hence colour image is converted to gray scale. Genetically Optimized Hard C Means (GOHCM) has been used for segmentation. Depending on the spectral value, the pixels are classified as urban area, bare soil, forest & vegetation and water regions by GOHCM.
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利用遗传优化Hard C方法进行卫星图像解译
本文介绍了利用遗传优化硬C均值(GOHCM)解译任意给定卫星图像的任务。GOHCM已被用于分割卫星图像。图像分割是将像素划分为同质类或聚类的过程,使同一聚类中的项目尽可能相似,而不同聚类中的项目尽可能不相似。分割的最基本属性是单色图像的亮度幅度和彩色图像的颜色分量。由于任何给定的彩色图像中都有超过1600万种颜色,因此很难对图像的整个颜色进行分析。因此,彩色图像被转换成灰度图像。遗传优化硬C均值(GOHCM)已被用于分割。GOHCM根据光谱值将像元分为城区、裸土区、森林植被区和水区。
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