首页> 外文会议>IEEE Conference on Industrial Electronics and Applications; 20070523-25; Harbin(CN) >The Research of Satellite Cloud Image Recognition Base on Variational Method and Texture Feature Analysis
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The Research of Satellite Cloud Image Recognition Base on Variational Method and Texture Feature Analysis

机译:基于变分和纹理特征分析的卫星云图像识别研究

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摘要

Recently, the development of satellite cloud image processing technology has become very quick; the research aspects concentrate on judge the cloud type and classify the cloud mainly. These image processing methods relate to the subject category like image processing and pattern recognition etc; it has become one of the fields of most quickly development in the research of satellite image processing technology. In satellite cloud image, texture is an very important feature, since satellite cloud image has clear texture structure, the computer texture analysis provide perfect future for study and analyze all kinds of satellite cloud image. Variational method is a new image segmentation method development in recent years, which is adapt to modeling and extract deformable contour of random shape. In satellite cloud image, recognize the target object has great application meaning. In this paper, using computer image texture analysis technology combine with Variational theory, extract and analyze the texture feature of familiar cloud and clear sky in satellite cloud image, adopt co-occurrence matrix base on statistic feature, compute the texture parameter like energy, entropy, contrast, correlation and local steady, at last, carry through the automatic recognition experiment which using the active contour model base on Variational theory, and acquire better effect of recognition to satellite cloud image.
机译:近来,卫星云图像处理技术的发展很快。研究方面主要集中在判断云类型和对云进行分类。这些图像处理方法涉及诸如图像处理和模式识别等主题类别。它已经成为卫星图像处理技术研究中发展最快的领域之一。在卫星云图像中,纹理是非常重要的特征,由于卫星云图像具有清晰的纹理结构,因此计算机纹理分析为研究和分析各种卫星云图像提供了完美的未来。变分法是近年来发展起来的一种新的图像分割方法,它适用于建模和提取任意形状的可变形轮廓。在卫星云图像中,识别目标物体具有重要的应用意义。本文采用计算机图像纹理分析技术,结合变分理论,提取并分析了卫星云图像中熟悉云和晴空的纹理特征,采用基于统计特征的共现矩阵,计算出能量,熵等纹理参数。最后,通过基于变分理论的主动轮廓模型,进行了自动识别实验,取得了较好的识别效果。

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