Assessment of very high spatial resolution satellite image segmentations
Assessment of very high spatial resolution satellite image segmentations
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DOI：
10.14358/pers.71.11.1285
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发表时间：
2005-11-01
影响因子：
1.3
通讯作者：
Wolff, E
中科院分区：
文献类型：
--
作者：
Carleer, AP;Debeir, O;Wolff, E
自1999年以来，空间分辨率极高的卫星数据更加详细地显示了地球表面。然而，每像素多光谱分类技术的信息提取被证明是非常复杂的，由于内部变异性增加的土地覆盖单位和光谱分辨率的弱点。分类前的图像分割被提出作为一种替代方法，但在过去的20年中开发了大量的各种分割算法，并比较它们在非常高的空间分辨率图像上的实现是必要的。在这项研究中，四个算法从两个主要组的分割算法（基于边界和基于区域）进行了评估和比较。为了比较算法，每个算法进行了评估与经验的差异评估方法。这种评价是用Ikonos全色图像的视觉分割进行的。结果表明，参数的选择非常重要，对分割结果有很大的影响。所选择的基于边界的算法对噪声或纹理敏感。使用基于区域的算法可以获得更好的结果，但可能存在对比对象之间的过渡区的问题。
Since 1999, very high spatial resolution satellite data represent the surface of the Earth with more detail. However, information extraction by per pixel multispectral classification techniques proves to be very complex owing to the internal variability increase in land-cover units and to the weakness of spectral resolution. Image segmentation before classification was proposed as an alternative approach, but a large variety of segmentation algorithms were developed during the last 20 years, and a comparison of their implementation on very high spatial resolution images is necessary. In this study, four algorithms from the two main groups of segmentation algorithms (boundary based and region-based) were evaluated and compared. In order to compare the algorithms, an evaluation of each algorithm was carried out with empirical discrepancy evaluation methods. This evaluation is carried out with a visual segmentation of Ikonos panchromatic images. The results show that the choice of parameters is very important and has a great influence on the segmentation results. The selected boundary-based algorithms are sensitive to the noise or texture. Better results are obtained with region-based algorithms, but a problem with the transition zones between the contrasted objects can be present.