Color and wavelet based identification of urban and agricultural area using texture features in satellite images

M. Umaselvi, S. S. Kumar, M. Athithya
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Abstract

This paper represents unsupervised method of color based segmentation using clustering to classify vegetated and urban area in Satellite images. Now a day due to the progresses in spatial resolution of satellite imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more important. In this work, one method proposed a segmentation of various clusters by La*b* color space and Texture Feature was analyzed. The Second method proposed a segmentation of image by wavelet and Texture Feature was analyzed. Algorithm is verified for simulated images and applied for a selected satellite image processing.
基于颜色和小波的卫星图像纹理特征的城乡区域识别
提出了一种基于颜色的无监督聚类分割方法,对卫星图像中的植被和城市区域进行分类。随着卫星图像空间分辨率的不断提高,基于图像分段分析的地理信息生成和更新方法变得越来越重要。本文分析了一种利用La*b*颜色空间和纹理特征对不同聚类进行分割的方法。提出了一种基于小波和纹理特征的图像分割方法。对仿真图像进行了验证,并将算法应用于选定的卫星图像处理。
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