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

机译:基于卫星图像纹理特征的城市和农业面积的彩色与小波识别

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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 anlyzed. Algorithm is verified for simulated images and applied for a selected satellite image processing.
机译:本文代表了使用聚类对卫星图像中的植被和城市地区进行分类的彩色分割的无监督方法。 现在,由于卫星图像的空间分辨率的进展,用于生成和更新地理信息的基于分段的图像分析方法变得越来越重要。 在这项工作中,一个方法提出了由La&#x2217的各种集群的分割; b∗ 分析了颜色空间和纹理特征。 第二种方法提出了通过小波和纹理特征进行图像的分割。 验证算法用于模拟图像并应用于所选择的卫星图像处理。

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