Histogram thresholding for unsupervised change detection of remote sensing images
Histogram thresholding for unsupervised change detection of remote sensing images
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DOI：
10.1080/01431161.2010.507793
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发表时间：
2011-01-01
影响因子：
3.4
通讯作者：
Ghosh, Ashish
中科院分区：
文献类型：
--
作者：
Patra, Swarnajyoti;Ghosh, Susmita;Ghosh, Ashish
变化检测问题可以被看作是一个无监督的分类问题，有两个类对应于变化和不变的地区。图像差分是一种广泛使用的变化检测方法。它是基于产生一个差异的图像，表示与研究区域中的每个像素相关联的光谱变化矢量的模的想法。为了自动分离出差异图像中的变化类和不变类，可以使用任何无监督技术。持牌是其中最便宜的技术之一。然而，在阈值化方法中，选择最佳阈值不是一项简单的任务。在这项工作中，几个非模糊和模糊直方图阈值技术进行了研究和比较的变化检测问题。实验结果，进行不同的多时相遥感图像（获取事件之前和之后），被用来评估每个阈值技术的有效性。在所有的阈值技术研究，刘的模糊熵其次是Kapur的熵被认为是最强大的技术。
The change-detection problem can be viewed as an unsupervised classification problem with two classes corresponding to changed and unchanged areas. Image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vectors associated with each pixel in the study area. To separate out the changed and unchanged classes in the difference image automatically, any unsupervised technique can be used. Thresholding is one of the cheapest techniques among them. However, in thresholding approaches, selection of the best threshold value is not a trivial task. In this work, several non-fuzzy and fuzzy histogram thresholding techniques are investigated and compared for the change-detection problem. Experimental results, carried out on different multitemporal remote sensing images (acquired before and after an event), are used to assess the effectiveness of each of the thresholding techniques. Among all the thresholding techniques investigated here, Liu's fuzzy entropy followed by Kapur's entropy are found to be the most robust techniques.