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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >A FRAMEWORK FOR THE EVALUATION OF MULTI-SPECTRAL IMAGE SEGMENTATION
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A FRAMEWORK FOR THE EVALUATION OF MULTI-SPECTRAL IMAGE SEGMENTATION

机译：评估多光谱图像分割的框架

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摘要

A general framework for testing the quality of the segmentation of a multi-spectral satellite image is proposed. The method is based on the production of synthetic images with the spectral characteristics of the image pixels extracted from a signature multi-spectral image. The knowledge of the exact location of objects in the synthetic image provides a reference segmentation, which allows for a quantitative evaluation of a segmentation algorithm applied to the image. The Hammoude metric and the external similarity indices Rand, Corrected Rand and Jaccard are used. A practical application was carried out to illustrate the value of the proposed method. Two satellite images, from SPOT HRG and Landsat TM, were used to extract the spectral signature of 8 land cover types. Six test images were produced using all 8 land cover classes and with two different sub-sets with 5 classes. The segmentation results provided by a standard algorithm were compared with the reference or expected segmentation. An evaluation of the parameters used in the eCognition software segmentation algorithm was also carried out, using the proposed indices.
机译：提出了一种用于测试多光谱卫星图像的分割质量的一般框架。该方法基于与从签名多谱图像提取的图像像素的光谱特性的合成图像的产生。合成图像中对象的确切位置的知识提供了参考分段，其允许对应用于图像的分割算法进行定量评估。使用Hammoude度量标准和外部相似度指数RAND，更正RAND和JACCARD。进行了实际应用以说明所提出的方法的价值。从点HRG和LANDSAT TM，两种卫星图像用于提取8种陆地覆盖类型的光谱特征。使用所有8个陆地覆盖类和具有5个类的两个不同的子集生产六个测试图像。将标准算法提供的分段结果与参考或预期分割进行了比较。还使用所提出的指标进行了认知软件分段算法中使用的参数的评估。

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