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首页> 外文期刊>European Journal of Remote Sensing >Textural segmentation of remotely sensed images using multiresolution analysis for slum area identification
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Textural segmentation of remotely sensed images using multiresolution analysis for slum area identification

机译：利用多分辨率分析对贫民窟面积识别的微量感测图像的质量分割

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Many cities in developing countries are facing rapid growth of dynamic slum areas but often lack detailed information and analysis on these informal settlements. Multiresolution analysis (MRA) has been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a texture-based segmentation scheme using newly developed multiresolution methods for slum area identification. The proposed method is tested on remotely sensed images where textural information in terms of statistical moments and energy are extracted at various scales and in different directions with the help of curvelet and contourlet transforms. The results are compared with wavelet-based MRA method of segmentation. Accuracy assessment is performed for segmented images, and comparative analysis is carried out in terms of class-wise and overall accuracies. It is found that the proposed method shows better class-discriminating power as compared to existing methods and overall classification accuracy of 91.4–95.4%.
机译：发展中国家的许多城市正面临着动态贫民区的快速增长，但往往缺乏对这些非正式定居点的详细信息和分析。多分辨率分析（MRA）已成功用于纹理分析。纹理分析在文献中广泛讨论，但大多数不采用多分辨率策略的方法不能利用在各种空间尺度处发生纹理的事实。本文采用了一种基于纹理的分割方案，用于贫民窟区域识别的新开发的多分辨率方法。在远程感测的图像上测试所提出的方法，其中在统计时刻和能量方面的纹理信息在不同的尺度和不同方向上提取，并且在曲线和轮廓件的帮助下提取。将结果与基于小波的MRA分割方法进行比较。对分段图像进行准确性评估，并在课堂和整体精度方面进行比较分析。结果发现，与现有方法和整体分类准确性为91.4-95.4％，所提出的方法显示出更好的类别辨别力。

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