SEDMI: Saliency based edge detection in multispectral images
SEDMI: Saliency based edge detection in multispectral images
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DOI：
10.1016/j.imavis.2011.06.002
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
V. Dinh;R. Leitner;P. Paclík;M. Loog;R. Duin
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文献类型：
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作者：
V. Dinh;R. Leitner;P. Paclík;M. Loog;R. Duin
多光谱图像的边缘检测是一个难点，因为不同的光谱带可能包含不同的边缘。现有的方法是基于像素与其相邻像素之间的强度变化来局部计算像素的边缘强度。因此，它们常常不能检测到嵌入在背景杂波中的物体的边缘或只出现在某些波段的物体。我们提出了SEDMI方法，旨在通过考虑图像中边缘的显著性来克服这一问题。基于边缘在图像中是罕见事件的观察，我们将边缘检测问题转化为在新定义的特征空间中检测具有小概率事件的问题。特征空间由各光谱通道的空间梯度大小构成。由于边缘通常局限于该特征空间中的小而孤立的聚类，因此可以根据其所属聚类的大小计算像素的边缘强度，或者该像素是具有小概率事件的置信度值。在多个多光谱数据集上的实验结果以及与其他方法的比较表明，该方法在检测嵌入背景杂波或只出现在少数波段的目标方面具有鲁棒性。
Detecting edges in multispectral images is difficult because different spectral bands may contain different edges. Existing approaches calculate the edge strength of a pixel locally, based on the variation in intensity between this pixel and its neighbors. Thus, they often fail to detect the edges of objects embedded in background clutter or objects which appear in only some of the bands. We propose SEDMI, a method that aims to overcome this problem by considering the salient properties of edges in an image. Based on the observation that edges are rare events in the image, we recast the problem of edge detection into the problem of detecting events that have a small probability in a newly defined feature space. The feature space is constructed by the spatial gradient magnitude in all spectral channels. As edges are often confined to small, isolated clusters in this feature space, the edge strength of a pixel, or the confidence value that this pixel is an event with a small probability, can be calculated based on the size of the cluster to which it belongs. Experimental results on a number of multispectral data sets and a comparison with other methods demonstrate the robustness of the proposed method in detecting objects embedded in background clutter or appearing only in a few bands.